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Tiara, Tiara, and Fandi Yulian Pamuji. "KOMPARASI USABILITY CHATGPT VS GEMINI AI BERDASARKAN ISO/IEC 9126 DAN NIELSEN MODEL MENGGUNAKAN METODE USABILITY TESTING." JUSIM (Jurnal Sistem Informasi Musirawas) 9, no. 1 (2024): 89–100. http://dx.doi.org/10.32767/jusim.v9i1.2285.
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Artificial Intelligence (AI), khususnya yang berbasis chatbot sedang nge-trend dan mengalami perkembangan yang sangat pesat akhir-akhir ini, menyebabkan munculnya fenomena perdebatan dalam memilih chatbot AI yang terbaik. Oleh karena itu peneliti ingin mengkomparasi chatbot yang paling banyak digunakan berdasarkan data dari Visual Capitalist, yaitu ChatGPT dan Gemini AI. Peneliti ingin mengetahui chatbot mana yang terbaik khusunya dari segi usabilitynya berdasarkan nilai dan persentasenya. Metode yang digunakan pada penelitian ini yaitu metode usability testing dengan melakukan skenario tugas dan menyebarkan kuesioner dengan pertanyaan dari parameter gabungan Nielsen Model dan ISO/IEC 9126. Temuan yang didapatkan dari hasil komparasi ChatGPT dan Gemini AI didapatkan bahwa pengguna ChatGPT dapat menyelesaikan skenario tugas hanya 0,07 per detik, sedangkan Gemini AI 0,10 per detik, ini menunjukan bahwa dalam segi efficiency ChatGPT lebih unggul dibandingkan Gemini AI. Sedangkan hasil variabel learnabilitymenunjukan Gemini AI lebih unggul dengan hasil 92% dibandingkan ChatGPT yang memperoleh hasil 90% dikategori diatas rata-rata. Dan untuk variabel satisfaction, Gemini AI juga lebih unggul dengan nilai 85,52% yang termasuk kategori sangat baik, sedangkan ChatGPT memperoleh hasil 81,86% berada di kategori baik. Sehingga, secara keseluruhan Gemini AI lebih unggul dibandingkan ChatGPT, Gemini AI unggul pada variabel leanability dan satisfaction, sedangkan ChatGPT unggul di variabel efficiency. Penelitian ini berkontribusi pada bidang software quality control khususnya diobjek yang diteliti dalam hal studi komparasi sehingga diharapkan dapat menjadi insight dalam pengambilan keputusan ketika ingin memilih antara ChatGPT atau Gemini AI. Kata kunci— Usability; Usability Testing; ChatGPT; Gemini AI
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Nazarius, Atong, Ferry Saputra, Nova Noor Kamala sari, and Viktor Handrianus Pranatawijaya. "PENERAPAN GEMINI AI DALAM PEMBUATAN DESKRIPSI PRODUK E-COMMERCE." JATI (Jurnal Mahasiswa Teknik Informatika) 8, no. 3 (2024): 3721–25. http://dx.doi.org/10.36040/jati.v8i3.9670.
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Diera digital saat ini, e-commerce menjadi salah satu industri besar yang berkembang pesat, dengan didorong karna adanya perkembangan teknologi dan Artificial Intelligence (AI). Salah satu pemodelan AI yang inovatif adalah Gemini AI, yang dikembangkan oleh google. Pada penelitian ini penggunaan Gemini AI dalam konteks e-commerce, terkhusus dalam pembuatan deskripsi produk yang dimana Gemini AI mampu mengotomatisasinya, dengan dapat meghasilkan deskripsi yang informatif dan menarik bagi pengguna. Keunggulan dengan adanya Gemini AI pada pembuatan deskripsi produk adalah dapat meminimalisir waktu dan tenaga, serta dapat menghasilkan deskripsi yang lebih liberal terhadap sebuah produk di platform e-commerce. Pada pengimplementasian Gemini AI, memerlukan sebuah akses ke API Key Gemini, yang dibuka oleh google untuk pengembang dan bisnis, dengan memahami dan memanfaatkan potensi dari Gemini AI, pemilik bisnis dari sebuah e-commerce dapat meningkatkan efisiensi terhadap biaya operasional dan meningkatkan penjualan melalui deskripsi produk yang informatf dan menarik bagi konsumen.
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Shirley Ling Jen. "Revolutionising Essay Writing Using Artificial Intelligence." Journal of Information Systems Engineering and Management 10, no. 4s (2025): 244–50. https://doi.org/10.52783/jisem.v10i4s.495.
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Introduction: With the emergence of Artificial Intelligence (AI), there is a need to integrate the latest technological tools such as generative AI in teaching essay writing. Thus, this study was carried out to examine the usefulness of Gemini AI in assisting students’ essay writing. Objectives: The Research Objectives (RO) for this study are to examine: secondary school students’ Perceived Usefulness (PU) of Gemini secondary school students’ Perceived Ease of Use (PEOU) of Gemini Methods: The Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) of Gemini AI were analysed qualitatively through journal entries, participant observation, questionnaires and interviews. Results: The findings show that students showed favourable responses towards the use of Gemini AI in assisting them in their essay writing. This is because Gemini AI does not only help students with generalisation of ideas and gaining new vocabulary, it also provides flexibility for them to learn at their own pace. Conclusion: Hopefully, this study will bring useful insights to practitioners and researchers in using AI to teach essay writing to students.
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Gupta, Sanjana, Sweety Pandre, Rishabh Biswal, Saniya Khan, and Gargi Mishra. "ALVoyage – An AI Travel Planner." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (2024): 834–39. http://dx.doi.org/10.22214/ijraset.2024.61695.
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Abstract: The document discusses the ALVoyage AI travel planner, which integrates with Gemini and Weather APIs and utilizes Firebase authentication. ALVoyage offers personalized travel planning by leveraging AI technology to curate itineraries based on user preferences. It seamlessly integrates with the Gemini API to generate informative content and incorporates real-time weather data from the OpenWeatherMap API. Firebase authentication ensures user security and privacy. The document also highlights the benefits and key features of Gemini and OpenWeatherMap APIs. The methodology section outlines the system architecture and components of ALVoyage, including user authentication, UI/UX design, Gemini API integration, Weather API integration, and user reviews. The results and discussion section emphasizes the system's performance, user feedback, and the future scope of the project. The conclusion highlights the potential of AI-powered travel planning apps and the need for further research and development in the field.
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Ananda, Dhio Rizky, and Maryati Salmiah. "Students’ Perception on AI Technology : Gemini as a Writing Assistant Tool." Linguistics and ELT Journal 12, no. 1 (2024): 46. https://doi.org/10.31764/leltj.v12i1.24393.
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Student perceptions are very important to educators. Student perceptions lie in how students interpret their educational experiences. Perceptions are shaped by individual experiences, backgrounds and learning styles. AI is becoming increasingly common in many industries, including education, as technology advances. AI is used in education to provide learning content that suits the needs of students. One of them is Gemini. Gemini is a large language model created by Google AI. Gemini is an AI tool that can answer questions in an informative way, even when the questions are open-ended, challenging, or strange. The purpose of this research is to see students' perceptions of the use of AI technology, namely Gemini, as an auxiliary tool in the English Writing process. This research is a qualitative research. This study used the semi-structured interview method, where there are main questions and follow-up questions, where the follow-up questions will be based on the answers to the main questions given at the beginning. This study involved 9 EFL students out of 30 students who had been selected. Most students already have their own AI technology applications, so they think they are reluctant to adapt to AI technology applications like Gemini. But on the other hand, Gemini has many features that are very helpful in the writing process. Although for now students prefer AI technology applications that they have used before compared to Gemini, but when viewed from the perceptions given by students, researchers believe Gemini will be used as a writing tool in the future. This research can provide another option for students to facilitate the writing process so that they do not depend on the AI technology applications they have used before.
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Celik, Ahmet. "Artificial intelligence in Otorhinolaryngology practice: Comparative performance of ChatGPT and Gemini AI." Journal of Clinical Trials and Experimental Investigations 3, no. 4 (2024): 156–62. https://doi.org/10.5281/zenodo.14617672.
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<strong>Objective:</strong> This study aims to evaluate the accuracy of ChatGPT and Gemini AI in the field of otorhinolaryngology. <strong>Materials and methods:</strong> This study evaluated the performance of ChatGPT 4.0 and Gemini AI in answering 150 multiple-choice questions evenly distributed across otorhinolaryngology domains: ear, nose, and throat. Both models were tested under standardized conditions, with their responses compared to an answer key. The true and false answers were evaluated. <strong>Results:</strong> For ear-related questions, ChatGPT correctly answered 34 (68%), while Gemini AI correctly answered 33 (66%) (p=0.832). For nose-related questions, both models achieved identical results: 34 correct answers (68%) and 16 incorrect answers (32%) (p=1.000). For throat-related questions, ChatGPT provided 34 correct answers (68%) compared to Gemini AI's 38 correct answers (76%) (p=0.373). Overall, ChatGPT achieved 102 correct answers (68%) and Gemini AI achieved 105 (70%), with no statistically significant difference between the models (p=0.708). The total correct answers across all topics were 207 (69%), and incorrect answers were 91 (31%). Binary logistic regression showed no significant differences in performance between the AI models or topics, confirming their comparable accuracy in otorhinolaryngology question sets. <strong>Conclusion:</strong> ChatGPT 4.0 and Gemini AI demonstrated comparable performance in answering otorhinolaryngology questions, with no statistically significant differences observed across ear, nose, and throat topics. Both models achieved high accuracy rates (ChatGPT: 68%, Gemini AI: 70%), suggesting their potential applicability in clinical decision-making and supporting otorhinolaryngology-related diagnostics.
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Majid, Muhamad Nurkolis, Akmaliyah Akmaliyah, and Muhamad Bagus Hermawan. "Perbandingan Hasil Terjemah Arab – Indonesia Antara ChatGPT dan Gemini AI." Jurnal Ihtimam 7, no. 02 (2024): 1–21. https://doi.org/10.36668/jih.v7i02.1004.
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Abstract: As the Arabic language spreads internationally, translation activities have become increasingly important, especially in the academic world where foreign literature is often encountered. With the advancement of technology, translation is no longer limited to the use of conventional dictionaries, but is growing rapidly thanks to artificial intelligence (AI). AI platforms, such as ChatGPT and Gemini AI, are able to translate quickly and are used by many people, including academics, to facilitate the translation process. The purpose of this research is to find out which artificial intelligence (AI) platform provides better translation results and provide platform recommendations for translation activities. This research is a descriptive qualitative research, this research uses Arabic text samples taken from the idhoat book which are then translated into Indonesian using ChatGPT and Gemini AI translation machines. The results of this study indicate that ChatGPT tends to provide more accurate and comprehensive translation results compared to Gemini AI, it is shown by ChatGPT's accurate translation results and is able to translate the entire text entered into the ChatGPT translation machine. As for the translation produced by Gemini AI, it tends to be inaccurate and not comprehensive in translating texts, it is shown by errors in translation and not translating the entire text entered into the Gemini AI translation machine. Although ChatGPT produces better translations, users still need to check and edit the results to correct errors, either using manual or electronic dictionaries. Keywords: ChatGPT, Gemini AI, Translation
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Azmi, Khairul, and Rahmah Fithriani. "Students’ Perceptions of Gemini Ai Effectiveness in Academic Writing." IDEAS: Journal on English Language Teaching and Learning, Linguistics and Literature 13, no. 1 (2025): 74–93. https://doi.org/10.24256/ideas.v13i1.6278.
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This research investigates students' perceptions of the effectiveness of Gemini AI in English academic writing, addressing the gap in the literature on AI-powered writing tools for EFL students. A qualitative case study involving 26 English education students from a university in North Sumatra explored how Gemini AI impacts their academic writing. Data was collected through questionnaires, analysis of students' academic writing files, and semi-structured interviews. The findings highlight the significant impact of Gemini AI on students' academic writing, particularly in improving structure, coherence, and revision skills. The study also reveals challenges related to source attribution and plagiarism concerns. This study contributes valuable insights into the potential of AI-powered tools like Gemini to enhance EFL students' academic writing capabilities and provides practical implications for educators and developers.
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Fitriani, Novia, Nadya Revelin Putri, Ardhy Dwiwicaksono, Viktor Handrianus Pranatawijaya, and Nova Noor Kamala Sari. "MEMPERKAYA PEMROGRAMAN WEB SISTEM KASIR DENGAN TEKNOLOGI AI: IMPLEMENTASI API GEMINI." JATI (Jurnal Mahasiswa Teknik Informatika) 8, no. 4 (2024): 5736–41. http://dx.doi.org/10.36040/jati.v8i4.9669.
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Artikel ini membahas penerapan AI dalam pemrograman web untuk meningkatkan efisiensi sistem kasir coffe shop melalui API. Dalam era digital, teknologi telah mengubah berbagai aspek bisnis, termasuk industri coffe shop. Sistem kasir berbasis web memanfaatkan API Gemini AI untuk otomatisasi deskripsi produk, mempercepat transaksi, dan memberikan informasi konsisten serta menarik kepada pelanggan. Penelitian ini menggunakan metode waterfall, dari analisis kebutuhan hingga pengujian sistem untuk memastikan kualitas dan kinerja optimal. Dari hasil pembahasan, penerapan API Gemini AI pada sistem kasir coffee shop membawa dampak positif signifikan. Integrasi API Gemini AI memungkinkan otomatisasi pembuatan deskripsi produk, mengurangi beban kerja manual bagi kasir, dan meningkatkan efisiensi operasional. Dengan algoritma AI, API dapat menghasilkan deskripsi produk konsisten, relevan, menarik, dan dalam waktu singkat sekitar 2,47s, sehingga meningkatkan pengalaman pelanggan dengan informasi yang jelas. Penerapan API Gemini tidak hanya meningkatkan efisiensi operasional dan kualitas layanan, tetapi juga membantu coffee shop tetap kompetitif. Meskipun memerlukan pemeliharaan rutin untuk kinerja optimal, manfaat dari penerapan API Gemini terlihat jelas dalam meningkatnya kepuasan pelanggan terhadap informasi menu yang diberikan kasir dan kesuksesan bisnis secara keseluruhan.
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Seth, Ishith, Gianluca Marcaccini, Kaiyang Lim, et al. "Management of Dupuytren’s Disease: A Multi-Centric Comparative Analysis Between Experienced Hand Surgeons Versus Artificial Intelligence." Diagnostics 15, no. 5 (2025): 587. https://doi.org/10.3390/diagnostics15050587.
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Background: Dupuytren’s fibroproliferative disease affecting the hand’s palmar fascia leads to progressive finger contractures and functional limitations. Management of this condition relies heavily on the expertise of hand surgeons, who tailor interventions based on clinical assessment. With the growing interest in artificial intelligence (AI) in medical decision-making, this study aims to evaluate the feasibility of integrating AI into the clinical management of Dupuytren’s disease by comparing AI-generated recommendations with those of expert hand surgeons. Methods: This multicentric comparative study involved three experienced hand surgeons and five AI systems (ChatGPT, Gemini, Perplexity, DeepSeek, and Copilot). Twenty-two standardized clinical prompts representing various Dupuytren’s disease scenarios were used to assess decision-making. Surgeons and AI systems provided management recommendations, which were analyzed for concordance, rationale, and predicted outcomes. Key metrics included union accuracy, surgeon agreement, precision, recall, and F1 scores. The study also evaluated AI performance in unanimous versus non-unanimous cases and inter-AI agreements. Results: Gemini and ChatGPT demonstrated the highest union accuracy (86.4% and 81.8%, respectively), while Copilot showed the lowest (40.9%). Surgeon agreement was highest for Gemini (45.5%) and ChatGPT (42.4%). AI systems performed better in unanimous cases (accuracy up to 92.0%) than in non-unanimous cases (accuracy as low as 35.0%). Inter-AI agreements ranged from 75.0% (ChatGPT-Gemini) to 48.0% (DeepSeek-Copilot). Precision, recall, and F1 scores were consistently higher for ChatGPT and Gemini than for other systems. Conclusions: AI systems, particularly Gemini and ChatGPT, show promise in aligning with expert surgical recommendations, especially in straightforward cases. However, significant variability exists, particularly in complex scenarios. AI should be viewed as complementary to clinical judgment, requiring further refinement and validation for integration into clinical practice.
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Rachmat, Nur, and Dorie P. Kesuma. "Implementasi LLM Gemini Pada Pengembangan Aplikasi Chatbot Berbasis Android." Jurnal Ilmu Komputer (JUIK) 4, no. 1 (2024): 40. https://doi.org/10.31314/juik.v4i1.2831.
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Perkembangan LLM sebagai bagian dari teknologi kecerdasan buatan saat ini sudah berkembang dengan sangat pesat, dimana implementasinya telah banyak digunakan pada berbagai jenis aplikasi dan digunakan di banyak bidang. Kehadiran LLM seperti GPT melahirkan banyak aplikasi AI seperti chatbot yang jauh lebih cerdas dibandingkan dengan aplikasi-aplikasi chatbot sebelumnya. Selain GPT dari OpenAI, terdapat pula LLM seperti PaLM 2 dari Google yang digunakan di Bard, dan kini Google kembali merilis LLM terbaru mereka yang bernama Gemini, LLM terbaru dan paling mutakhir dari Google yang mampu menyaingi GPT. Agar Gemini bisa digunakan oleh pengembang aplikasi yang tertarik untuk membuat aplikasi AI mereka sendiri, maka Google membuka akses ke kunci API Gemini. Penelitian ini dilakukan untuk mengetahui bagaimana implementasi dari kunci API Gemini dapat dilakukan pada aplikasi AI, khususnya aplikasi chatbot dan apakah aplikasi chatbot yang dibangun dengan menggunakan Gemini dapat berfungsi sesuai dengan fungsionalitas dari LLM Gemini itu sendir. Di dalam pengembangan aplikasi chatbot dengan menggunakan LLM Gemini ini penulis menggunakan metodologi pengembangan prototyping, dan aplikasi chatbot yang dikembangkan berjalan pada platform Android. Setelah aplikasi chatbot yang dimaksud selesai dibuat dan diuji, didapati hasil bahwa aplikasi chatbot yang dihasilkan berfungsi sebagaimana yang diharapkan dan mampu memanfaatkan fungsionalitas dari LLM Gemini.
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Handoko, Rifky Mustaqim Handoko, Wahyuni Putra, Ryan Delon Pratama, Nova Noor Kamalasari, and Viktor Handrianus Pranatawijaya. "IMPLEMENTASI GEMINI AI DALAM PENGEMBANGAN APLIKASI E-COMMERCE MOBILE GENERATE DESKRIPSI PRODUK." ProTekInfo(Pengembangan Riset dan Observasi Teknik Informatika) 11, no. 1 (2024): 21–25. http://dx.doi.org/10.30656/protekinfo.v11i1.8595.
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Intisari— Dalam era digital yang berkembang pesat, e-commerce menjadi salah satu industri yang penting untuk menjangkau konsumen secaraefektif. Artikel ini membahas tentang implementasi teknologi artificial intelligence (AI), khususnya Gemini AI, dalam pengembangan aplikasie-commerce mobile menggunakan Flutter SDK. Teknologi AI digunakan untuk secara otomatis menghasilkan deskripsi produk berdasarkanpilihan pengguna, meningkatkan efisiensi dan pengalaman pengguna. Integrasi API Gemini AI memungkinkan aplikasi untuk memberikaninformasi produk yang informatif dan menarik. Hasil penelitian menunjukkan bahwa penggunaan teknologi AI dalam e-commerce dapatmemberikan nilai tambah dalam menyajikan informasi produk secara efektif dan efisien kepada pengguna, serta memberikan rekomendasi untukpengembangan aplikasi e-commerce yang lebih baik di masa depan.Kata kunci— Artificial Intelligence, Deskripsi Produk, Mobile, Flutter, Gemini
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Burhan, Burhan, Abdillah SAS, A. Rizal, Achmad Fajar Muhammad, Restu Januarty, and Rampeng Rampeng. "INOVASI PEMBELAJARAN DI ERA DIGITAL: MEMPERKUAT KOMPETENSI GURU MELALUI GEMINI ARTIFICIAL INTELLIGENCE (AI)." DEVOSI 6, no. 1 (2025): 27–40. https://doi.org/10.33558/devosi.v6i1.10054.
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Pelatihan desain pembelajaran berbasis Gemini AI di SD Inpres Monginsidi Kota Makassar bertujuan untuk meningkatkan kompetensi guru dalam memanfaatkan teknologi kecerdasan buatan (AI) guna menciptakan pembelajaran yang lebih inovatif, adaptif, dan efektif. Pelatihan ini diikuti oleh 30 guru yang diberi materi tentang pengenalan Gemini AI, cara menggunakan platform tersebut untuk merancang pembelajaran yang sesuai dengan kebutuhan siswa, serta praktik langsung dalam merancang dan mengimplementasikan rencana pembelajaran berbasis teknologi. Pelatihan desain pembelajaran berbasis Gemini AI di SD Inpres Monginsidi menggunakan pendekatan partisipatif dan kolaboratif. Metode pelatihan juga diterapkan dalam program pengabdian terdiri dari beberapa tahapan yang dirancang untuk memaksimalkan pemahaman dan keterampilan guru dalam menggunakan teknologi Gemini AI. Hasil pelatihan menunjukkan peningkatan kemampuan guru dalam mendesain pembelajaran digital yang lebih interaktif dan personal. Meskipun terdapat tantangan dalam hal infrastruktur dan kesiapan mental, pelatihan ini berhasil memperluas pemahaman guru mengenai teknologi kecerdasan buatan dan penggunaannya di bidang pendidikan. Dengan demikian, hasil pelatihan ini berkontribusi signifikan dalam penguatan kompetensi guru serta peningkatan kualitas pembelajaran di sekolah dasar.
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Shukla, Ashutosh. "AI-Powered Flat Finder: A Real Estate Search Assistant using Gemini, React, and Firebase." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem48549.
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ABSTRACT — The real estate industry is increasingly leveraging artificial intelligence to enhance property discovery and decision-making. This paper presents a web-based application that serves as an AI-powered flat-finding assistant. Built using ReactJS for the frontend, Gemini (Google’s generative AI) for conversational intelligence, and Firebase for backend and database management, the application enables users to interact with a chatbot to find flats matching their preferences. The system allows users to ask natural language questions, which are interpreted by Gemini AI, and matched with property listings stored in Firebase. A custom training layer is added to Gemini to ensure relevant and consistent answers based on predefined intents. The paper details the architecture, data flow, and interaction design of the platform, emphasizing real-time communication and personalized responses. This work illustrates how AI-driven interfaces can modernize property searches, improve client engagement, and streamline real estate operations. Keywords — Real Estate, Artificial Intelligence, Gemini AI, Firebase, ReactJS, Flat Finder, Property Recommendation, Chatbot.
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Marpaung, Syafaruddin. "Boosting High School Students' Speaking Proficiency with Gemini AI." English Journal Antartika 2, no. 1 (2024): 8–15. https://doi.org/10.70052/eja.v2i1.543.
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Abstract: This study investigates the impact of Gemini AI on improving speaking proficiency among 30 high school students using Classroom Action Research (CAR) with a mixed-methods approach. The study observed progressive improvements in speaking proficiency over three iterative cycles, with students’ average scores rising from 65 in the first cycle to 85 by the third cycle. These gains were reflected in enhanced fluency, vocabulary, and confidence. Gemini AI provided personalized speaking practice and real-time feedback, leading to increased engagement and adaptive learning. The study concludes that integrating AI into speaking classes can enhance language proficiency through iterative feedback and adaptation. Keywords: Boosting, High School Students, Speaking Proficiency, Gemini AI
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Azizoğlu, Mustafa, and Sergey Klyuev. "A Comparative Study on the Question-Answering Proficiency of Artificial Intelligence Models in Bladder-Related Conditions: An Evaluation of Gemini and ChatGPT 4.o." Medical Records 7, no. 1 (2025): 201–5. https://doi.org/10.37990/medr.1601528.
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Aim: The rapid evolution of artificial intelligence (AI) has revolutionized medicine, with tools like ChatGPT and Google Gemini enhancing clinical decision-making. ChatGPT's advancements, particularly with GPT-4, show promise in diagnostics and education. However, variability in accuracy and limitations in complex scenarios emphasize the need for further evaluation of these models in medical applications. This study aimed to assess the accuracy and agreement between ChatGPT 4.o and Gemini AI in identifying bladder-related conditions, including neurogenic bladder, vesicoureteral reflux (VUR), and posterior urethral valve (PUV). Material and Method: This study, conducted in October 2024, compared ChatGPT 4.o and Gemini AI's accuracy on 51 questions about neurogenic bladder, VUR, and PUV. Questions, randomly selected from pediatric surgery and urology materials, were evaluated using accuracy metrics and statistical analysis, highlighting AI models' performance and agreement. Results: ChatGPT 4.o and Gemini AI demonstrated similar accuracy across neurogenic bladder, VUR, and PUV questions, with true response rates of 66.7% and 68.6%, respectively, and no statistically significant differences (p>0.05). Combined accuracy across all topics was 67.6%. Strong inter-rater reliability (κ=0.87) highlights their agreement. Conclusion: This study highlights the comparable accuracy of ChatGPT-4.o and Gemini AI across key bladder-related conditions, with no significant differences in performance.
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Ali, Muttaqin Kholis, Al Muhtadibillah Ali, and Arrahmil Hasanah. "Efektivitas Fitur ChatGPT, Gemini dan Claude AI dalam Membantu Guru Membuat Bahan Ajar." PEDAGOGIC: Indonesian Journal of Science Education and Technology 4, no. 1 (2024): 58–71. http://dx.doi.org/10.54373/ijset.v4i1.1649.
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This study aims to analyze the effectiveness of the features of ChatGPT, Gemini AI, and Claude AI in helping teachers create teaching materials at SMA Negeri 1 Tambangan. This study uses an experimental design of participants who are divided into four groups (three AI groups and one control group), this study compares the quality of the teaching materials produced with the help of the three AI platforms. Data collection methods include pre-test, post-test, questionnaire, and analysis of AI usage activity logs. The results of the analysis show that Gemini AI has better results in improving the quality of teaching materials, with an average score increase of 20.5 points, compared to ChatGPT (13.4 points) and Claude AI (14.6 points). Gemini AI's advantages are mainly seen in the aspects of contextualization and personalization of learning materials. The results of the technology acceptance analysis show that the adoption rate is high for Gemini AI, with the highest scores in perceived usefulness (6.4/7) and behavioral intention (6.6/7). Implementation challenges include the need for teacher training and limited infrastructure in several regions. This research provides important implications for the development of policies and practices for the use of AI in education in Indonesia, emphasizing the importance of integrating AI technology that takes into account the local context and specific needs of students.
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Bedel, Hatice Aslı, Cihan Bedel, Fatih Selvi, Ökkeş Zortuk, and Yusuf Karanci. "Emergency Medicine Assistants in the Field of Toxicology, Comparison of ChatGPT-3.5 and GEMINI Artificial Intelligence Systems." Acta medica Lituanica 31, no. 2 (2024): 18. https://doi.org/10.15388/amed.2024.31.2.18.
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Objective: Artificial intelligence models human thinking and problem-solving abilities, allowing computers to make autonomous decisions. There is a lack of studies demonstrating the clinical utility of GPT and Gemin in the field of toxicology, which means their level of competence is not well understood. This study compares the responses given by GPT-3.5 and Gemin to those provided by emergency medicine residents.Methods: This prospective study was focused on toxicology and utilized the widely recognized educational resource ‘Tintinalli Emergency Medicine: A Comprehensive Study Guide’ for the field of Emergency Medicine. A set of twenty questions, each with five options, was devised to test knowledge of toxicological data as defined in the book. These questions were then used to train ChatGPT GPT-3.5 (Generative Pre-trained Transformer 3.5) by OpenAI and Gemini by Google AI in the clinic. The resulting answers were then meticulously analyzed.Results: 28 physicians, 35.7% of whom were women, were included in our study. A comparison was made between the physician and AI scores. While a significant difference was found in the comparison (F=2.368 and p<0.001), no significant difference was found between the two groups in the post-hoc Tukey test. GPT-3.5 mean score is 9.9±0.71, Gemini mean score is 11.30±1.17 and, physicians’ mean score is 9.82±3.70 (Figure 1).Conclusions: It is clear that GPT-3.5 and Gemini respond similarly to topics in toxicology, just as resident physicians do.
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Padmaja, K., Apoorva S. Bhat, Esther J. Kenn, and Jeevitha L. Prakash. "MOCK INTERVIEW SYSTEM." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–6. https://doi.org/10.55041/ijsrem40009.
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Abstract -Sconti-AI is an innovative platform revolutionizing exam preparation and interview practice by harnessing advanced AI technology. It offers detailed feedback, scores, and explanations for mock tests, optimizing learning outcomes and improving performance. Its standout feature, a mock interview tool, uses Generative AI and image recognition to detect cheating, analyze emotional responses, and provide personalized insights for better interview preparation. Built with robust technologies like React, Python- Flask, and Gemini-AI, the platform ensures scalability, reliability, and user engagement. Key Words: LLM, Media Pipe, Gemini AI, AI, ML and Image Processing.
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Hancı, Volkan, Bişar Ergün, Şanser Gül, Özcan Uzun, İsmail Erdemir, and Ferid Baran Hancı. "Assessment of readability, reliability, and quality of ChatGPT®, BARD®, Gemini®, Copilot®, Perplexity® responses on palliative care." Medicine 103, no. 33 (2024): e39305. http://dx.doi.org/10.1097/md.0000000000039305.
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There is no study that comprehensively evaluates data on the readability and quality of “palliative care” information provided by artificial intelligence (AI) chatbots ChatGPT®, Bard®, Gemini®, Copilot®, Perplexity®. Our study is an observational and cross-sectional original research study. In our study, AI chatbots ChatGPT®, Bard®, Gemini®, Copilot®, and Perplexity® were asked to present the answers of the 100 questions most frequently asked by patients about palliative care. Responses from each 5 AI chatbots were analyzed separately. This study did not involve any human participants. Study results revealed significant differences between the readability assessments of responses from all 5 AI chatbots (P < .05). According to the results of our study, when different readability indexes were evaluated holistically, the readability of AI chatbot responses was evaluated as Bard®, Copilot®, Perplexity®, ChatGPT®, Gemini®, from easy to difficult (P < .05). In our study, the median readability indexes of each of the 5 AI chatbots Bard®, Copilot®, Perplexity®, ChatGPT®, Gemini® responses were compared to the “recommended” 6th grade reading level. According to the results of our study answers of all 5 AI chatbots were compared with the 6th grade reading level, statistically significant differences were observed in the all formulas (P < .001). The answers of all 5 artificial intelligence robots were determined to be at an educational level well above the 6th grade level. The modified DISCERN and Journal of American Medical Association scores was found to be the highest in Perplexity® (P < .001). Gemini® responses were found to have the highest Global Quality Scale score (P < .001). It is emphasized that patient education materials should have a readability level of 6th grade level. Of the 5 AI chatbots whose answers about palliative care were evaluated, Bard®, Copilot®, Perplexity®, ChatGPT®, Gemini®, their current answers were found to be well above the recommended levels in terms of readability of text content. Text content quality assessment scores are also low. Both the quality and readability of texts should be brought to appropriate recommended limits.
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Shelke, Shailaja. "AgroCare: AI-Powered Crop Management System." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem48399.
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Agriculture faces growing challenges due to climate change, resource limitations, and rising global demand. AgroCare is an intelligent decision-support system designed to assist farmers through real-time crop recommendations, fertilizer suggestions, disease detection, and yield prediction. Leveraging powerful APIs like Gemini, OpenWeather, and Plant.id, combined with deep learning techniques including Convolutional Neural Networks (CNNs) and Large Language Models (LLMs), the system ensures precision and personalization. The platform features a Firebase-authenticated user interface and rich data visualizations, offering actionable insights and sustainable farming practices. Keywords—AgroCare, crop recommendation, fertilizer suggestion, disease detection, yield prediction, Gemini API, Plant.id, OpenWeather, CNN, LLM, precision agriculture, smart farming, Firebase, Chart.js.
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Pressman, Sophia M., Sahar Borna, Cesar A. Gomez-Cabello, Syed Ali Haider, and Antonio Jorge Forte. "AI in Hand Surgery: Assessing Large Language Models in the Classification and Management of Hand Injuries." Journal of Clinical Medicine 13, no. 10 (2024): 2832. http://dx.doi.org/10.3390/jcm13102832.
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Background: OpenAI’s ChatGPT (San Francisco, CA, USA) and Google’s Gemini (Mountain View, CA, USA) are two large language models that show promise in improving and expediting medical decision making in hand surgery. Evaluating the applications of these models within the field of hand surgery is warranted. This study aims to evaluate ChatGPT-4 and Gemini in classifying hand injuries and recommending treatment. Methods: Gemini and ChatGPT were given 68 fictionalized clinical vignettes of hand injuries twice. The models were asked to use a specific classification system and recommend surgical or nonsurgical treatment. Classifications were scored based on correctness. Results were analyzed using descriptive statistics, a paired two-tailed t-test, and sensitivity testing. Results: Gemini, correctly classifying 70.6% hand injuries, demonstrated superior classification ability over ChatGPT (mean score 1.46 vs. 0.87, p-value < 0.001). For management, ChatGPT demonstrated higher sensitivity in recommending surgical intervention compared to Gemini (98.0% vs. 88.8%), but lower specificity (68.4% vs. 94.7%). When compared to ChatGPT, Gemini demonstrated greater response replicability. Conclusions: Large language models like ChatGPT and Gemini show promise in assisting medical decision making, particularly in hand surgery, with Gemini generally outperforming ChatGPT. These findings emphasize the importance of considering the strengths and limitations of different models when integrating them into clinical practice.
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Zaimah, Nely Rahmawati, Risti Kamila Wening Estu, Syarifatul Fitri Hidayah, Syamsul Hadi, and Aiden Button. "Harnessing Gemini for Arabic Mastery: Educators' and Learners' Views." Alibbaa': Jurnal Pendidikan Bahasa Arab 5, no. 2 (2024): 166–88. http://dx.doi.org/10.19105/ajpba.v5i2.14808.
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This study aims to explore the perspectives on the reliability of Gemini in Arabic language studies, supported by a comprehensive literature review for each segment. Adopting a qualitative phenomenological approach, the research focuses on examining the experiences and perceptions of academics regarding the use of the Gemini chatbot. The research population includes 120 students studying Quranic Sciences and Exegesis at STAI Al-Anwar Rembang, purposefully selected for their collective proficiency in the Arabic language. Additionally, 11 Arabic language teachers in Rembang are actively involved in developing a structured teaching framework enriched by Gemini. The result highlights the importance of collaboration among linguists, educators, and tech developers in leveraging Gemini, an AI tool, for effective Arabic language education, while addressing accuracy and privacy concerns. The researcher anticipates that this study will provide insights into integrating AI tools like Gemini into Indonesian curricula, enhancing Arabic language learning. It seeks to contribute to the discourse on technology-enhanced language education and its practical implications.
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Al Islam, Faisal, and Ameera Binty Nazmul. "ChatGPT Versus Gemini: A Comparative Analysis of the Factors Influencing Academic Performance among Bangladeshi University Students." Journal of Humanities and Social Sciences Studies 7, no. 2 (2025): 24–34. https://doi.org/10.32996/jhsss.2025.7.2.3.
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The global expansion of accessible Gen AI tools, specifically Large Language Models (LLMs) such as ChatGPT and Gemini is transforming the paradigm of higher education. Bangladesh is not exempt from this as the favorability of ChatGPT and Gemini is increasing substantially among Bangladeshi university students given its availability and ability to generate exclusive content, and comprehensive responses within seconds. However, the core differences between ChatGPT and Gemini in accessing and processing information lead them to provide exclusive experience and expertise influencing students’ productivity and interest. By analyzing the factors influencing Bangladeshi students’ use of ChatGPT and Gemini, this research intends to provide critical insights and contribute to developing policies for incorporating AI ethically in the national curriculum addressing students’ needs. This study adopted quantitative methods with a questionnaire survey. The paper identified the key factors that differentiate the use of ChatGPT and Gemini for academic purposes among university students in Bangladesh which will contributes to transformative changes in Bangladesh’s education development.
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Faisal, Al Islam, and Binty Nazmul Ameera. "ChatGPT Versus Gemini: A Comparative Analysis of the Factors Influencing Academic Performance among Bangladeshi University Students." International Journal of Innovative Science and Research Technology (IJISRT) 10, no. 1 (2025): 951–58. https://doi.org/10.5281/zenodo.14744536.
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The global expansion of accessible Gen AI tools, specifically Large Language Models (LLMs) such as ChatGPT and Gemini is transforming the paradigm of higher education. Bangladesh is not exempt from this as the favorability of ChatGPT and Gemini is increasing substantially among Bangladeshi university students given its availability and ability to generate exclusive content, and comprehensive responses within seconds. However, the core differences between ChatGPT and Gemini in accessing and processing information lead them to provide exclusive experience and expertise influencing students’ productivity and interest. By analyzing the factors influencing Bangladeshi students’ use of ChatGPT and Gemini, this research intends to provide critical insights and contribute to developing policies for incorporating AI ethically in the national curriculum addressing students’ needs. This study adopted quantitative methods with a questionnaire survey. The paper identified the key factors that differentiate the use of ChatGPT and Gemini for academic purposes among university students in Bangladesh which will contributes to transformative changes in Bangladesh’s education development.
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Anil, Kumar Jonnalagadda, Kumar Myakala Praveen, and Bura Chiranjeevi. "The AI Trifecta: Revolutionizing Innovation Across Disciplines." International Journal of Innovative Science and Research Technology (IJISRT) 10, no. 1 (2025): 593–601. https://doi.org/10.5281/zenodo.14651187.
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The rapid evolution of artificial intelligence (AI) has ushered in a new era of innovation, with tools like Gemini, Copilot, and ChatGPT redefining boundaries across diverse fields. Dubbed the ”AI Trifecta,” these technologies offer comple- mentary capabilities: Gemini excels at understanding and gener- ating multimodal data, Copilot provides context-aware coding assistance, and ChatGPT facilitates human-like conversations and creative content generation. This study explores their synergistic potential in revolution- izing workflows across research, development, and education. For instance, researchers can leverage Gemini for data analysis, Copilot to automate coding tasks, and ChatGPT to commu- nicate findings effectively. Case studies demonstrate how this trio enhances creativity, streamlines processes, and accelerates knowledge discovery at unprecedented scales. We also address key challenges, including ethical consider- ations, human oversight, and the integration of these systems into existing workflows. By presenting actionable insights and future directions, this paper highlights the transformative power of the ”AI Trifecta” in establishing AI-driven collaboration as a cornerstone of innovation across disciplines.
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Eneva, Yordanka, and Bora Dogan. "Evaluation of Medical Diagnosis Capabilities of Three Artificial Intelligence Models – ChatGPT-3.5, Google Gemini, Microsoft Copilot: Sustainable Development Goals (SDGs)." Journal of Lifestyle and SDGs Review 5, no. 2 (2025): e03545. https://doi.org/10.47172/2965-730x.sdgsreview.v5.n02.pe03545.
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Objectives: This study aims to assess and compare the diagnostic accuracy of three artificial intelligence (AI) models—ChatGPT-3.5, Microsoft Copilot, and Google Gemini—through their performance on clinical vignettes. Theoretical Framework: Building on prior research into the application of AI in healthcare, particularly in diagnostic support, this study examines the potential of AI models to aid clinicians by providing accurate medical diagnoses, thus supporting decision-making in clinical contexts. Methodology: A meta-analysis was conducted, followed by a comparative analysis using 34 clinical vignettes from Texas Tech University Health Sciences Center. Each AI model’s responses were evaluated for accuracy in diagnosing medical cases, and statistical significance was tested using the chi-square test. Results and Discussion: ChatGPT-3.5 achieved the highest diagnostic accuracy (70.59%), outperforming Google Gemini (61.76%) and Microsoft Copilot (35.29%). ChatGPT-3.5 provided concise answers, while Google Gemini and Microsoft Copilot included disclaimers and additional recommendations. Chi-square analysis confirmed significant differences in performance, highlighting variations in diagnostic capabilities across models. Research Implications: These findings underscore the importance of model selection when integrating AI into clinical workflows. AI models show promise in diagnostics but vary in approach and accuracy, warranting further refinement. Originality/Value: This study is among the first to compare the diagnostic accuracy of ChatGPT-3.5, Google Gemini, and Microsoft Copilot, contributing valuable insights into AI’s application in healthcare diagnostics and supporting evidence for its potential role in enhancing patient care.
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Kacer, Emine Ozdemir. "Evaluating AI-based breastfeeding chatbots: quality, readability, and reliability analysis." PLOS ONE 20, no. 3 (2025): e0319782. https://doi.org/10.1371/journal.pone.0319782.
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Background In recent years, expectant and breastfeeding mothers commonly use various breastfeeding-related social media applications and websites to seek breastfeeding-related information. At the same time, AI-based chatbots-such as ChatGPT, Gemini, and Copilot-have become increasingly prevalent on these platforms (or on dedicated websites), providing automated, user-oriented breastfeeding guidance. Aim The goal of our study is to understand the relative performance of three AI-based chatbots: ChatGPT, Gemini, and Copilot, by evaluating the quality, reliability, readability, and similarity of the breastfeeding information they provide. Methods Two researchers evaluated the information provided by three different AI-based breastfeeding chatbots: ChatGPT version 3.5, Gemini, and Copilot. A total of 50 frequently asked questions about breastfeeding were identified and used in the study, divided into two categories (Baby-Centered Questions and Mother-Centered Questions), and evaluated using five scoring criteria, including the Quality Information Provision for Patients (EQIP) scale, the Simple Measure of Gobbledygook (SMOG) scale, the Similarity Index (SI), the Modified Dependability Scoring System (mDISCERN), and the Global Quality Scale (GQS). Results The evaluation of AI chatbots’ answers showed statistically significant differences across all criteria (p < 0.05). Copilot scored highest on the EQIP, SMOG, and SI scales, while Gemini excelled in mDISCERN and GQS evaluations. No significant difference was found between Copilot and Gemini for mDISCERN and GQS scores. All three chatbots demonstrated high reliability and quality, though their readability required university-level education. Notably, ChatGPT displayed high originality, while Copilot exhibited the greatest similarity in responses. Conclusion AI chatbots provide reliable answers to breastfeeding questions, but the information can be hard to understand. While more reliable than other online sources, their accuracy and usability are still in question. Further research is necessary to facilitate the integration of advanced AI in healthcare.
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Labrague, Leodoro J. "Utilizing Artificial Intelligence–Based Tools for Addressing Clinical Queries: ChatGPT Versus Google Gemini." Journal of Nursing Education 63, no. 8 (2024): 556–59. http://dx.doi.org/10.3928/01484834-20240426-01.
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Background: Artificial intelligence (AI)–based text generators, such as ChatGPT (OpenAI) and Google Bard (now Google Gemini), have demonstrated proficiency in predicting words and providing responses to various questions. However, their performance in answering clinical queries has not been well assessed. This comparative analysis aimed to assess the capabilities of ChatGPT and Google Gemini in addressing clinical questions. Method: Separate interactions with ChatGPT and Google Gemini were conducted to obtain responses to the clinical question, posed in a PICOT (patient, intervention, comparison, outcome, time) format. To ascertain the accuracy of the information provided by the AI chat bots, a thorough examination of full-text articles was conducted. Results: Although ChatGPT exhibited relative accuracy in generating bibliographic information, it displayed some inconsistencies in clinical content. Conversely, Google Gemini generated citations and summaries that were entirely fabricated. Conclusion: Despite generating responses that may appear credible, both AI-based tools exhibited factual inaccuracies, raising substantial concerns about their reliability as potential sources of clinical information. [ J Nurs Educ . 2024;63(8):556–559.]
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Luzano, Jay Fie P. "Reshaping Mathematics Instruction Via Impact of AI Chatbots on Secondary Education Pre-service Teachers." International Journal of Studies in Education and Science 5, no. 3 (2024): 233–45. http://dx.doi.org/10.46328/ijses.97.
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This study assessed the level of impact of AI Chatbots (ChatGPT, Gemini, and Perplexity) in Mathematics Education. The study employed a descriptive research design where structured survey questionnaires were used and administered to ninety (90) Secondary Education Pre-service Teachers at Bukidnon State University, Philippines. Results showed that the level of impact of the AI Chatbots (ChatGPT, Gemini, and Perplexity) in Mathematics Education on the nine (9) factors is Very High, namely; (1) Effectiveness ; (2) Engagement ; (3) Accessibility ; (4) Personalization ; (5) Feedback Quality ; (6) Confidence Building ; (7) Time Efficiency ; (8) Adaptability ; and (9) Student Satisfaction . The findings demonstrate a positive student assessment on AI chatbots (ChatGPT, Gemini, and Perplexity) in mathematics education, consistently rated highly across various factors, indicating their significant impact on reshaping instruction, providing valuable support, enhancing learning experiences, and boosting confidence and proficiency in math, underscoring the transformative potential of AI technology and its essential integration into educational practices to address students' evolving needs in the digital age.
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Muna, Bunga Laelatul, Sudianto Sudianto, and Muhammad Lulu Latif Usman. "SiAkif-Bots: Gemini AI for Academic Service Chatbots." Journal of Applied Engineering and Technological Science (JAETS) 6, no. 2 (2025): 1237–53. https://doi.org/10.37385/jaets.v6i2.6728.
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Academic services are an important element in education, as they provide students with access to information and support. At Telkom University Purwokerto, there are obstacles to the efficiency of academic services, especially due to information delays and the high burden of onsite services. To overcome this challenge, a Telegram-based chatbot, "SiAkif," was developed using the Large Language Model (LLM) model from Gemini AI. Gemini AI's selection is based on its ability to understand complex conversational contexts and generate accurate and relevant responses. This research aims to implement the Telegram chatbot that utilizes Gemini AI for Indonesian-language academic services. The implementation showed satisfactory results, with the chatbot "SiAkif" recording an average BLEU score of 0.88, which reflects good performance and response. This chatbot effectively reduces information delays, expands service accessibility, and improves student experience in interacting with institutions. Through "SiAkif," the institution is expected to strengthen the interaction between students and academic services, making it a potential solution for digital transformation in education.
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Fabijan, Artur, Michał Chojnacki, Agnieszka Zawadzka-Fabijan, et al. "AI-Powered Western Blot Interpretation: A Novel Approach to Studying the Frameshift Mutant of Ubiquitin B (UBB+1) in Schizophrenia." Applied Sciences 14, no. 10 (2024): 4149. http://dx.doi.org/10.3390/app14104149.
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The application of artificial intelligence (AI) in the analysis of molecular biology data is becoming increasingly widespread. The Western Blot (WB) technique, a cornerstone in proteomic research, facilitates the identification and analysis of proteins, such as the frameshift mutant of ubiquitin B (UBB+1). In our study, we attempted to assess the potential of four different AI models—Gemini, Gemini Advanced, Microsoft Copilot, and ChatGPT 4—in the analysis of WB imagery containing UBB+1, derived from peripheral blood studies of patients suffering from schizophrenia. Participants, all male and diagnosed with schizophrenia, were recruited from the Specialist Psychiatric Care Team of Babinski Hospital in Lodz. After obtaining their informed consent, blood samples were collected and transported to the laboratory of the Department of Medical Biochemistry at the Medical University of Lodz. The samples were processed, synthesis of Ub-48UBB+1 dimers was performed, and the WB technique was applied. The result of the WB analysis, in the form of a photograph with basic labels but without a legend (JPG format), was implemented into ChatGPT 4, Microsoft Copilot, Gemini and Gemini Advanced. Following the implementation of the image, the command ‘Could you analyze the attached photo?’ was added, along with the protocol from Sample Preparation and Synthesis of Ub-48UBB+1 Dimers. The AI models effectively analyzed and interpreted WB images, with variations in their approaches and depth. Gemini excelled in detailing the WB process and biological significance of bands, while Gemini Advanced focused on specific band identification, especially Ub-48UBB+1 dimers. Microsoft Copilot provided a basic overview with less technicality, and ChatGPT 4 offered comprehensive band interpretations, linking them to patient samples and standards, thus confirming the hypothesis about the differing capabilities of these models. This discovery demonstrates the advanced capabilities of ChatGPT 4 and highlights the growing role of AI in scientific research, including the interpretation of results.
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Puspandari*, Diyas, Sri Prasetiyowati, and Yuliant Sibaroni. "untuk, dalam Training on The Use of AI to Increase Teacher Competency in Preparing The Learning Process." Dinamisia : Jurnal Pengabdian Kepada Masyarakat 9, no. 1 (2025): 34–41. https://doi.org/10.31849/dinamisia.v9i1.20929.
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Teachers, as educational personnel, are expected to enhance their competence in mastering information technology, which can later be utilized in classroom learning activities. However, in reality, teachers' knowledge and skills in mastering technology to support teaching and learning activities are still limited. Based on a survey conducted, teachers at SD Indriyasana, Baleendah, are eager to gain knowledge and skills in using open-source applications, such as Chat GPT, Gemini AI, and MajickPen AI, to support their tasks, both in teaching and administrative duties. Therefore, training and mentoring are needed in learning the use of these open-source applications to support teachers' activities. The aim of this training is to introduce and train teachers at SD Indriyasana, Baleendah, in using open-source applications based on artificial intelligence (AI), such as Chat GPT, Gemini AI, and MajickPen AI, to support teaching and administrative tasks. The method used involves training, direct practice by teachers, guided by speakers and instructors using AI-based open-source applications, namely ChatGPT, Gemini AI, and MajickPen. The implementation method includes the following steps: 1) user needs analysis, 2) literature study, 3) training module development, 4) module testing, 5) training implementation, 6) evaluation. The training results indicate that teachers acquired knowledge and practical experience in line with their needs, enabling them to use AI-based applications (Chat GPT, Gemini AI, and MajickPen AI) to support learning and administrative activities. It can be said that the training went well, as shown by the questionnaire results: 1) alignment of the training program with objectives (94%), 2) alignment of the program with partners' needs (71%), 3) adequacy of program implementation time (65%), 4) team’s ability to execute the program (88%), and 5) program sustainability (77%). This training activity is beneficial for both teachers and students because it can make learning more interesting and enjoyable for students. By utilizing AI-based applications, teachers can generate ideas for learning activities, create quizzes, or provide relevant and up-to-date teaching materials, thereby facilitating class preparation and easing teachers' administrative tasks.
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An-Naufal Nuha, Alfian, and Mutaqin Akbar. "OTOMOMATISASI PEMBUATAN DESKRIPSI EVENT MENGGUNAKAN GEMINI AI." JATI (Jurnal Mahasiswa Teknik Informatika) 9, no. 2 (2025): 2904–10. https://doi.org/10.36040/jati.v9i2.13223.
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Seiring dengan meningkatnya jumlah acara dan hiburan yang diselenggarakan, kebutuhan akan deskripsi event yang menarik dan informatif menjadi semakin penting, terutama bagi platform penjualan tiket online. Namun, pembuatan deskripsi acara secara manual sering kali memakan waktu dan rentan terhadap inkonsistensi dalam penyajian informasi. Oleh karena itu, penelitian ini bertujuan untuk mengembangkan sistem otomatisasi pembuatan deskripsi event menggunakan teknologi kecerdasan buatan (Artificial Intelligence/AI) melalui platform Gemini AI. Sistem ini dirancang untuk menghasilkan deskripsi event secara otomatis berdasarkan input yang diberikan oleh pengguna, seperti nama acara, waktu, lokasi, dan kategori acara. Data yang diinputkan akan diproses melalui API yang menghubungkan sistem dengan Gemini AI untuk menghasilkan deskripsi yang relevan dan informatif. Hasil deskripsi yang diperoleh kemudian disimpan dalam database dan ditampilkan pada halaman detail event secara otomatis. Pengujian dilakukan dengan mengukur efisiensi waktu, kualitas deskripsi, serta performa sistem dalam menangani permintaan simultan. Hasil penelitian menunjukkan bahwa sistem mampu menghasilkan deskripsi dalam waktu rata-rata 5-10 detik, dengan tingkat akurasi informasi yang tinggi. Meskipun deskripsi yang dihasilkan sudah cukup baik, dalam beberapa kasus diperlukan penyesuaian manual untuk menyesuaikan gaya bahasa dengan preferensi penyelenggara acara. Selain itu, pengujian skalabilitas menunjukkan bahwa sistem mampu menangani peningkatan jumlah permintaan tanpa mengalami penurunan performa yang signifikan. Dengan adanya sistem ini, diharapkan pemilik acara dan Event Organizer dapat lebih mudah dan cepat dalam menyusun deskripsi acara yang menarik dan informatif, sehingga dapat meningkatkan daya tarik acara bagi calon peserta.
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Rana, Neha, and Nitish Katoch. "AI for Biophysical Phenomena: A Comparative Study of ChatGPT and Gemini in Explaining Liquid–Liquid Phase Separation." Applied Sciences 14, no. 12 (2024): 5065. http://dx.doi.org/10.3390/app14125065.
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Recent advancements in artificial intelligence (AI), notably through generative pretrained transformers, such as ChatGPT and Google’s Gemini, have broadened the scope of research across various domains. Particularly, the role of AI in understanding complex biophysical phenomena like liquid–liquid phase separation (LLPS) is promising yet underexplored. In this study, we focus on assessing the application of these AI chatbots in understating LLPS by conducting various interactive sessions. We evaluated their performance based on the accuracy, response time, response length, and cosine similarity index (CSI) of their responses. Our findings show that Gemini consistently delivered more accurate responses to LLPS-related questions than ChatGPT. However, neither model delivered correct answers to all questions posed. Detailed analysis showed that Gemini required longer response times, averaging 272 words per response compared to ChatGPT’s 351. Additionally, the average CSI between the models was 0.62, highlighting moderate similarity. Despite both models showing potential to enhance scientific education in complex domains, our findings highlight a critical need for further refinement of these AI tools to improve their accuracy and reliability in specialized academic settings.
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Bin Akhtar, Zarif. "From bard to Gemini: An investigative exploration journey through Google’s evolution in conversational AI and generative AI." Computing and Artificial Intelligence 2, no. 1 (2024): 1378. http://dx.doi.org/10.59400/cai.v2i1.1378.
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The advent of artificial intelligence (AI) has significantly transformed various aspects of human life, particularly in information retrieval and assistance. This research presents a comprehensive evaluation of Gemini, previously known as Google Bard, a state-of-the-art AI chatbot developed by Google. Through a meticulous methodology encompassing both qualitative and quantitative approaches, this research aims to assess Gemini’s performance, usability, integration capabilities, ethical implications. Primary data collection methods, including user surveys and interviews, were utilized to gather towards the qualitative feedback on user experiences with Gemini, supplemented by secondary data analysis using tools such as Google Analytics to capture quantitative metrics. Performance evaluation involved benchmarking against other AI chatbots and technical analysis of Gemini’s architecture and training methods. User experience testing examined usability, engagement, and integration with Google Workspace and third-party services. Ethical considerations regarding data privacy, security, and biases in AI-generated content were also addressed, ensuring compliance with major regulations and promoting ethical AI practices. Acknowledging limitations and challenges inherent in the investigative exploration, data analysis was conducted using thematic and statistical methods to derive insights. The results and findings of this research offer valuable insights into the capabilities and limitations of Gemini, providing implications for future AI development, user interaction design, and ethical AI governance. By contributing to the ongoing discourse on AI advancements and their societal impact, this exploration facilitates informed decision-making and lays the groundwork for future research endeavors in the field of AI-driven conversational agents.
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Salmi Addin, Hanifatus, and Malta Nelisa. "Pemanfaatan Gemini Artificial Intelligence (AI) sebagai Sarana Pendukung Literasi Informasi bagi Mahasiswa Departemen Ilmu Informasi dan Perpustakaan." Jurnal Pustaka AI (Pusat Akses Kajian Teknologi Artificial Intelligence) 5, no. 1 (2025): 101–5. https://doi.org/10.55382/jurnalpustakaai.v5i1.956.
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Penelitian ini bertujuan untuk mendeskripsikan pemanfaataan Gemini AI sebagai sarana pendukung literasi media, literasi visual, dan literasi digital. Penelitian ini menggunakan jenis penelitian kualitatif dengan metode deskriptif. Informan pada penelitian ini yaitu 12 orang mahasiswa aktif Departemen Ilmu Informasi dan perpustakaan angkatan 2021-2024. Hasil penelitian ini menunjukkan bahwa pemanfaatan Gemini AI sebagai sarana pendukung literasi informasi bagi mahasiswa Departemen Ilmu Informasi dan Perpustakaan secara umum sangat membantu proses literasi informasi, baik itu literasi media, literasi visual, dan literasi digital.
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Kotmungkun, Siraprapa, Wichuta Chompurach, and Piriya Thaksanan. "OpenAI ChatGPT vs Google Gemini: A study of AI chatbots’ writing quality evaluation and plagiarism checking." English Language Teaching Educational Journal 7, no. 2 (2024): 90–108. http://dx.doi.org/10.12928/eltej.v7i2.11572.
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This study explores the writing quality of two AI chatbots, OpenAI ChatGPT and Google Gemini. The research assesses the quality of the generated texts based on five essay models using the T.E.R.A. software, focusing on ease of understanding, readability, and reading levels using the Flesch-Kincaid formula. Thirty essays were generated, 15 from each chatbot, and evaluated for plagiarism using two free detection tools—SmallSEOTools and Check-Plagiarism—as well as one paid tool, Turnitin. The findings revealed that both ChatGPT and Gemini performed well in terms of word concreteness but demonstrated weaknesses in narrativity. ChatGPT showed stronger performance in referential and deep cohesion, while Gemini excelled in narrativity, syntactic simplicity and word concreteness. However, a significant concern was the degree of plagiarism detected in texts from both AI tools, with ChatGPT's essays exhibiting a higher likelihood of plagiarism compared to Gemini’s. These findings highlight the potential limitations and risks associated with using AI-generated writing.
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Alabduljabbar, Reham. "User-centric AI: evaluating the usability of generative AI applications through user reviews on app stores." PeerJ Computer Science 10 (October 25, 2024): e2421. http://dx.doi.org/10.7717/peerj-cs.2421.
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This article presents a usability evaluation and comparison of generative AI applications through the analysis of user reviews from popular digital marketplaces, specifically Apple’s App Store and Google Play. The study aims to bridge the research gap in real-world usability assessments of generative AI tools. A total of 11,549 reviews were extracted and analyzed from January to March 2024 for five generative AI apps: ChatGPT, Bing AI, Microsoft Copilot, Gemini AI, and Da Vinci AI. The dataset has been made publicly available, allowing for further analysis by other researchers. The evaluation follows ISO 9241 usability standards, focusing on effectiveness, efficiency, and user satisfaction. This study is believed to be the first usability evaluation for generative AI applications using user reviews across digital marketplaces. The results show that ChatGPT achieved the highest compound usability scores among Android and iOS users, with scores of 0.504 and 0.462, respectively. Conversely, Gemini AI scored the lowest among Android apps at 0.016, and Da Vinci AI had the lowest among iOS apps at 0.275. Satisfaction scores were critical in usability assessments, with ChatGPT obtaining the highest rates of 0.590 for Android and 0.565 for iOS, while Gemini AI had the lowest satisfaction rate at −0.138 for Android users. The findings revealed usability issues related to ease of use, functionality, and reliability in generative AI tools, providing valuable insights from user opinions and feedback. Based on the analysis, actionable recommendations were proposed to enhance the usability of generative AI tools, aiming to address identified usability issues and improve the overall user experience. This study contributes to a deeper understanding of user experiences and offers valuable guidance for enhancing the usability of generative AI applications.
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Rane, Nitin, Saurabh Choudhary, and Jayesh Rane. "Gemini versus ChatGPT: applications, performance, architecture, capabilities, and implementation." Journal of Applied Artificial Intelligence 5, no. 1 (2024): 69–93. http://dx.doi.org/10.48185/jaai.v5i1.1052.
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This research paper presents an in-depth comparative examination of Gemini and ChatGPT, two prominent conversational AI models, exploring their respective applications, performance metrics, architectural variances, and overall capabilities. As conversational AI becomes increasingly prevalent across industries, comprehending the nuances of these models becomes pivotal for effective deployment. The paper initiates by outlining the wide array of applications for both Gemini and ChatGPT, spanning industries such as customer service, construction, finance, education, healthcare, and entertainment. It analyzes how each model addresses specific use cases, emphasizing their flexibility and potential impact across different sectors. Following this, the study assesses the performance of Gemini and ChatGPT through both empirical benchmarks and real-world deployment scenarios. Key metrics, including response coherence, accuracy, latency, and scalability, are scrutinized to gauge the models' ability to generate contextually appropriate and coherent responses in conversational contexts. Moreover, the paper elucidates the architectural distinctions between Gemini and ChatGPT, covering variances in training methodologies, model architectures, and underlying technologies. Understanding these architectural nuances provides deeper insights into the computational mechanisms underpinning each model's performance. Lastly, the paper explores the capabilities of Gemini and ChatGPT in handling complex linguistic phenomena, deciphering user intents, and sustaining engaging dialogues over prolonged interactions. This discussion encompasses language generation, sentiment analysis, context retention, and ethical considerations, shedding light on the potential of these models to facilitate meaningful human-computer interactions. Through this thorough comparative analysis, the research contributes to the ongoing conversation surrounding conversational AI systems. It offers valuable insights into the strengths and limitations of Gemini and ChatGPT, empowering stakeholders to make informed decisions regarding their optimal utilization across diverse applications.
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Chiu, Edwin Kwan-Yeung, Siddharth Sridhar, Samson Sai-Yin Wong, et al. "Generative Artificial Intelligence Models in Clinical Infectious Disease Consultations: A Cross-Sectional Analysis Among Specialists and Resident Trainees." Healthcare 13, no. 7 (2025): 744. https://doi.org/10.3390/healthcare13070744.
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Background/Objectives: The potential of generative artificial intelligence (GenAI) to augment clinical consultation services in clinical microbiology and infectious diseases (ID) is being evaluated. Methods: This cross-sectional study evaluated the performance of four GenAI chatbots (GPT-4.0, a Custom Chatbot based on GPT-4.0, Gemini Pro, and Claude 2) by analysing 40 unique clinical scenarios. Six specialists and resident trainees from clinical microbiology or ID units conducted randomised, blinded evaluations across factual consistency, comprehensiveness, coherence, and medical harmfulness. Results: Analysis showed that GPT-4.0 achieved significantly higher composite scores compared to Gemini Pro (p = 0.001) and Claude 2 (p = 0.006). GPT-4.0 outperformed Gemini Pro and Claude 2 in factual consistency (Gemini Pro, p = 0.02; Claude 2, p = 0.02), comprehensiveness (Gemini Pro, p = 0.04; Claude 2, p = 0.03), and the absence of medical harm (Gemini Pro, p = 0.02; Claude 2, p = 0.04). Within-group comparisons showed that specialists consistently awarded higher ratings than resident trainees across all assessed domains (p < 0.001) and overall composite scores (p < 0.001). Specialists were five times more likely to consider responses as “harmless”. Overall, fewer than two-fifths of AI-generated responses were deemed “harmless”. Post hoc analysis revealed that specialists may inadvertently disregard conflicting or inaccurate information in their assessments. Conclusions: Clinical experience and domain expertise of individual clinicians significantly shaped the interpretation of AI-generated responses. In our analysis, we have demonstrated disconcerting human vulnerabilities in safeguarding against potentially harmful outputs, which seemed to be most apparent among experienced specialists. At the current stage, none of the tested AI models should be considered safe for direct clinical deployment in the absence of human supervision.
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Stephenson-Moe, Christoph A., Benjamin J. Behers, Rebecca M. Gibons, et al. "Assessing the quality and readability of patient education materials on chemotherapy cardiotoxicity from artificial intelligence chatbots: An observational cross-sectional study." Medicine 104, no. 15 (2025): e42135. https://doi.org/10.1097/md.0000000000042135.
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Artificial intelligence (AI) and the introduction of Large Language Model (LLM) chatbots have become a common source of patient inquiry in healthcare. The quality and readability of AI-generated patient education materials (PEM) is the subject of many studies across multiple medical topics. Most demonstrate poor readability and acceptable quality. However, an area yet to be investigated is chemotherapy-induced cardiotoxicity. This study seeks to assess the quality and readability of chatbot created PEM relative to chemotherapy-induced cardiotoxicity. We conducted an observational cross-sectional study in August 2024 by asking 10 questions to 4 chatbots: ChatGPT, Microsoft Copilot (Copilot), Google Gemini (Gemini), and Meta AI (Meta). The generated material was assessed for readability using 7 tools: Flesch Reading Ease Score (FRES), Flesch-Kincaid Grade Level (FKGL), Gunning Fog Index (GFI), Coleman-Liau Index (CLI), Simple Measure of Gobbledygook (SMOG) Index, Automated Readability Index (ARI), and FORCAST Grade Level. Quality was assessed using modified versions of 2 validated tools: the Patient Education Materials Assessment Tool (PEMAT), which outputs a 0% to 100% score, and DISCERN, a 1 (unsatisfactory) to 5 (highly satisfactory) scoring system. Descriptive statistics were used to evaluate performance and compare chatbots amongst each other. Mean reading grade level (RGL) across all chatbots was 13.7. Calculated RGLs for ChatGPT, Copilot, Gemini and Meta were 14.2, 14.0, 12.5, 14.2, respectively. Mean DISCERN scores across the chatbots was 4.2. DISCERN scores for ChatGPT, Copilot, Gemini, and Meta were 4.2, 4.3, 4.2, and 3.9, respectively. Median PEMAT scores for understandability and actionability were 91.7% and 75%, respectively. Understandability and actionability scores for ChatGPT, Copilot, Gemini, and Meta were 100% and 75%, 91.7% and 75%, 90.9% and 75%, and 91.7% and 50%, respectively. AI chatbots produce high quality PEM with poor readability. We do not discourage using chatbots to create PEM but recommend cautioning patients about their readability concerns. AI chatbots are not an alternative to a healthcare provider. Furthermore, there is no consensus on which chatbots create the highest quality PEM. Future studies are needed to assess the effectiveness of AI chatbots in providing PEM to patients and how the capabilities of AI chatbots are changing over time.
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Zampatti, Stefania, Juliette Farro, Cristina Peconi, et al. "AI-Powered Neurogenetics: Supporting Patient’s Evaluation with Chatbot." Genes 16, no. 1 (2024): 29. https://doi.org/10.3390/genes16010029.
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Background/Objectives: Artificial intelligence and large language models like ChatGPT and Google’s Gemini are promising tools with remarkable potential to assist healthcare professionals. This study explores ChatGPT and Gemini’s potential utility in assisting clinicians during the first evaluation of patients with suspected neurogenetic disorders. Methods: By analyzing the model’s performance in identifying relevant clinical features, suggesting differential diagnoses, and providing insights into possible genetic testing, this research seeks to determine whether these AI tools could serve as a valuable adjunct in neurogenetic assessments. Ninety questions were posed to ChatGPT (Versions 4o, 4, and 3.5) and Gemini: four questions about clinical diagnosis, seven about genetic inheritance, estimable recurrence risks, and available tests, and four questions about patient management, each for six different neurogenetic rare disorders (Hereditary Spastic Paraplegia type 4 and type 7, Huntington Disease, Fragile X-associated Tremor/Ataxia Syndrome, Becker Muscular Dystrophy, and FacioScapuloHumeral Muscular Dystrophy). Results: According to the results of this study, GPT chatbots demonstrated significantly better performance than Gemini. Nonetheless, all AI chatbots showed notable gaps in diagnostic accuracy and a concerning level of hallucinations. Conclusions: As expected, these tools can empower clinicians in assessing neurogenetic disorders, yet their effective use demands meticulous collaboration and oversight from both neurologists and geneticists.
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Julianto, Indri Tri, Dede Kurniadi, Benedicto B. Balilo Jr, and Fauza Rohman. "THE ROLE OF FEATURE SELECTION IN ENHANCING THE ACCURACY OF AI ASSISTANT AUTO-LABELING." JURTEKSI (Jurnal Teknologi dan Sistem Informasi) 11, no. 1 (2024): 85–92. https://doi.org/10.33330/jurteksi.v11i1.3364.
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Abstract: The development of AI assistants such as Gemini and ChatGPT can significantly assist in daily human tasks. In the field of Sentiment Analysis, AI assistants can be utilized as an automated labeling alternative to provide positive, negative, or neutral sentiments within a dataset. This research aims to enhance the performance of AI assistants in automated labeling processes by employing the Feature Selection algorithm, specifically Forward Selection. The methodology involves utilizing the Naïve Bayes and K-NN algorithms, and subsequently improving accuracy through the Feature Selection algorithm. The evaluation is conducted using K-Fold Cross Validation. Research findings indicate an improvement in the accuracy of the best model, which is ChatGPT, when using the Naïve Bayes algorithm and Shuffled Sampling technique. The initial accuracy of 79.09% increased to 87.18% after Feature Selection was applied. This demonstrates the effectiveness of Feature Selection, particularly Forward Selection, in enhancing the accuracy performance of the model. Keywords: ai; assistant; chat gpt; feature selection; gemini. Abstrak: Pekembangan Asisten AI seperti Gemini dan Chat GPT dapat membantu pekerjaan manusia sehari-hari. Dalam bidang Analisis Sentimen, Asisten AI dapat digunakan sebagai alternatif pelabelan otomatis untuk memberikan sentimen positif, negatif atau netral dalam suatu dataset. Penlitian ini bertujuan untuk meningkatkan performa yang dihasilkan oleh Asisten AI dalam proses pelabelan otomatis menggunakan Algortima Feature Selection yaitu Forward Selection. Metode yang digunakan adalah dengan menggunakan Algoritma Naïve Bayes dan K-NN kemudian hasil akurasi akan ditingkatkan menggunkan Algoritma Feature Selection. Evaluasi yang digunakan adalah K-Fold Cross Validation. Hasil penelitian menunjukkan peningkatan akurasi model terbaik berada pada Chat GPT dengan menggunakan Algoritma Naïve Bayes dan Teknik Shuffled Sampling, dari nilai akurasi awal sebesar 79.09%, setelah ditingkatkan menggunakan Feature Selection, maka nilai akurasinya meningkat menjadi 87.18%. Hal ini membuktikan peran Feature Selection, dimana yang digunakan adalah Forward Selection dalam meningkatkan akurasi ternyata memang efektif dalam meningkatkan performa akurasi model. Kata kunci: ai; assisten; chat gpt; feature selection; gemini
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Ravi, Jayavadivel. "AI Powered Delivery Post Office Identification System." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47188.
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Abstract— Postal services are still important in today’s world, but many people find it hard to access them easily using mobile technology. This project introduces an AI-powered Android application developed using Kotlin, designed to help users quickly find nearby post offices, submit complaints, and get instant support through a chatbot. The app uses geo- mapping and the Gemini AI chatbot to provide real-time help and location tracking. It also includes an admin panel for managing user feedback and updating post office information. The main goal is to make postal services more user-friendly, accessible, and efficient by combining artificial intelligence and mobile technology. The app improves communication between users and the postal department while supporting smarter service delivery. Keywords— AI-Powered Application, Gemini chatbot, Geo- Mapping, Android Development, Kotlin, Postal Services, Complaint Management, Location Tracking, Mobile Application and Smart Postal System.
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Pavlik, Edward J., Dharani D. Ramaiah, Taylor A. Rives, Allison L. Swiecki-Sikora, and Jamie M. Land. "Replies to Queries in Gynecologic Oncology by Bard, Bing and the Google Assistant." BioMedInformatics 4, no. 3 (2024): 1773–82. http://dx.doi.org/10.3390/biomedinformatics4030097.
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When women receive a diagnosis of a gynecologic malignancy, they can have questions about their diagnosis or treatment that can result in voice queries to virtual assistants for more information. Recent advancement in artificial intelligence (AI) has transformed the landscape of medical information accessibility. The Google virtual assistant (VA) outperformed Siri, Alexa and Cortana in voice queries presented prior to the explosive implementation of AI in early 2023. The efforts presented here focus on determining if advances in AI in the last 12 months have improved the accuracy of Google VA responses related to gynecologic oncology. Previous questions were utilized to form a common basis for queries prior to 2023 and responses in 2024. Correct answers were obtained from the UpToDate medical resource. Responses related to gynecologic oncology were obtained using Google VA, as well as the generative AI chatbots Google Bard/Gemini and Microsoft Bing-Copilot. The AI narrative responses varied in length and positioning of answers within the response. Google Bard/Gemini achieved an 87.5% accuracy rate, while Microsoft Bing-Copilot reached 83.3%. In contrast, the Google VA’s accuracy in audible responses improved from 18% prior to 2023 to 63% in 2024. While the accuracy of the Google VA has improved in the last year, it underperformed Google Bard/Gemini and Microsoft Bing-Copilot so there is considerable room for further improved accuracy.
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Pla Ángel, Adrián. "Utilising Artificial Intelligence to Bolster and Refine the Production of Formal Letters in English Tailored for Students of Mechanical Engineering." Półrocznik Językoznawczy Tertium 9, no. 1 (2024): 250–73. http://dx.doi.org/10.7592/tertium.2024.9.1.289.
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Over the past decade, advancements in technology, notably Artificial Intelligence (AI), have significantly transformed educational methodologies and shifted the existing educational framework. Innovations like OpenAI’s ChatGPT and Gemini have garnered considerable attention, with their groundbreaking features potentially revolutionizing the realm of education, prompting concerns among educators and researchers (Grassini, 2023; Halaweh, 2023). This research endeavours to utilise AI, specifically ChatGPT and Gemini, to bolster and refine formal letter writing and email composition skills among first-cycle undergraduate students enrolled in the English for Engineering module at a Spanish university. Initially, students underwent a pre-test assessing their English proficiency and confidence levels, along with their familiarity with ChatGPT and other AI tools utilised for educational and writing purposes. Subsequently, students engaged in collaborative group tasks using Google Docs within Google Drive. Finally, a post-test was administered to gauge students' perceptions regarding their experience with English for Specific Purposes, particularly English for Mechanical Engineering, and their utilisation of ChatGPT and other AI tools for educational and writing purposes. Findings indicate that integrating AI can enhance students' grasp of written language accuracy in formal letter writing, particularly in inquiries, quotations, and complaint letters, facilitating better organisation and structure in their written correspondence in terms of style, accuracy, and communicative efficacy. This study suggests that investigations like the one presented here enable educators to explore the incorporation of AI, specifically ChatGPT and Gemini, to enrich students' writing proficiency, foster creativity, and cultivate technological aptitude within the classroom setting.
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S, Kartika. "Enhancing Writing Proficiency through AI-Powered Feedback: A Quasi-Experimental Study Using Google Gemini." LinguaEducare: Journal of English and Linguistic Studies 1, no. 2 (2024): 83–96. https://doi.org/10.63324/h6q1ak58.
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The use of Artificial Intelligence (AI) in education has gained attention for its potential to enhance student learning, yet limited research has focused on AI-powered tools for providing comprehensive feedback on writing. Most existing studies have concentrated on grammar correction, leaving a gap in understanding how AI can support broader writing development. This study aimed to examine the effect of Google Gemini, an AI-powered chatbot, on writing proficiency in a higher education setting, focusing on grammar, vocabulary, coherence, and task achievement. A quasi-experimental design was used with two groups of 40 students from the Sharia Faculty at UIN Raden Intan Lampung. The experimental group used Google Gemini for writing feedback, while the control group received traditional instruction. Both groups completed pre- and post-test writing tasks, assessed using a standardized rubric. Data were analyzed with paired-sample t-tests to compare improvements between groups. The results showed that the experimental group made significantly greater gains in writing proficiency than the control group, especially in grammar, vocabulary, and coherence. Students in the experimental group also reported higher satisfaction with the immediate, personalized feedback from the AI chatbot. The control group showed modest improvements with traditional feedback, but these were less pronounced. This study suggests that AI tools like Google Gemini can effectively improve writing skills by offering real-time, personalized feedback. It highlights the potential for AI to complement traditional teaching methods, though future research with larger and more diverse samples is needed to explore its impact on higher-order writing skills and across various educational contexts
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Muh. Subhan. "Exploring Public Sentiment Toward Artificial Intelligence Apps: A Case Study of ChatGPT, Gemini, and DeepSeek in Google Apps." Journal of Information Systems Engineering and Management 10, no. 19s (2025): 203–11. https://doi.org/10.52783/jisem.v10i19s.3007.
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Introduction: Artificial intelligence (AI) has witnessed rapid advancements in recent decades, impacting various sectors such as business, education, and entertainment. AI-based applications have become integral to daily interactions, with platforms like Google hosting popular applications such as ChatGPT, Gemini, and DeepSeek. These AI applications offer distinct approaches to technology but have the potential to influence public sentiment toward AI broadly. However, public perception remains diverse, with some embracing AI for its potential, while others express concerns regarding its implications, such as job displacement and privacy issues. Objectives: This study aims to explore the factors that shape public sentiment toward three AI applications—ChatGPT, Gemini, and DeepSeek. Specifically, it addresses the following research questions: (1) What factors influence public sentiment toward these AI applications? (2) How do the sentiments differ between these applications? (3) To what extent is public sentiment reflective of broader perceptions of AI technology? Methods: The research employs a case study approach, collecting user reviews from Google Play Store for ChatGPT, Gemini, and DeepSeek. Data preprocessing includes removing null entries, normalizing text, and performing tokenization. Sentiment classification is conducted using the Ekman’s Six Basic Emotions model, and sentiment analysis is enhanced using machine learning models, specifically Naive Bayes (NB) and Logistic Regression (LR). The models’ performance is evaluated based on AUC, Classification Accuracy (CA), F1 Score, Precision, Recall, and Matthews Correlation Coefficient (MCC). Results: The analysis reveals that User Interaction and App Performance are the primary factors influencing public sentiment. ChatGPT receives the highest level of positive sentiment, particularly for its interactive capabilities. While Gemini also receives favorable reviews, its focus on intelligent search results in slightly less positive sentiment compared to ChatGPT. DeepSeek displays a more mixed sentiment, with some users appreciating its depth in data analysis, but many expressing dissatisfaction with its user interaction. Sentiment analysis further demonstrates that Joy and Surprise were the dominant emotions for ChatGPT, whereas Fear and Disgust were less prevalent across all applications. Conclusions: This study concludes that user interaction and performance significantly drive public sentiment toward AI applications. While concerns over security and privacy exist, they are less influential compared to the experience users have with the application's functionality. The findings highlight the importance of enhancing user experience and performance for AI adoption. Additionally, the research provides insights into the need for further transparency regarding data privacy and the ethical use of AI.
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Rawa Bapir, Ahmed Mohammed Abdalqadir, Kamran Hassan Bhatti, et al. "Role of ChatGPT and Gemini in the Urology Field: A Case-Based Study." Barw Medical Journal, August 2, 2024. http://dx.doi.org/10.58742/bmj.v2i3.116.
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Introduction The healthcare sector is witnessing a transformation with the advent of artificial intelligence (AI), exemplified by ChatGPT and Gemini AI. These AI systems emulate human conversation and provide accurate medical responses. This study explores their integration into medical decision-making in the urology field. Methods The study presented a collection of 20 medical case scenarios, carefully crafted and revised by a team of authors in the field of urology. Each case was presented to ChatGPT and Gemini in September of 2023, and their responses were recorded and analyzed. Results Both AI tools displayed varying accuracy in diagnoses and management recommendations. ChatGPT failed in identifying congenital penile curvature, while Gemini succeeded. Conversely, ChatGPT excelled in recommending a management plan for renal artery aneurysms. Gemini outperformed in explaining iodinated contrast material toxicity. Both struggled with a bladder prolapse prevention question. Conclusion AI integration in urology is promising but has limitations. AI provides valuable insights but cannot replace human expertise. Research is vital to improve AI's role in urology. Clinicians should view AI suggestions as supplements to their judgment, fostering collaborative healthcare decisions.