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Relevant bibliographies by topics / AI in language learning

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Academic literature on the topic 'AI in language learning'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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Journal articles on the topic "AI in language learning"

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Ismayilli, Fatima. "REVOLUTIONIZING LANGUAGE LEARNING: INTEGRATION OF AI TO PERSONALIZED LANGUAGE LEARNING." Deutsche internationale Zeitschrift für zeitgenössische Wissenschaft 79 (May 7, 2024): 22–23. https://doi.org/10.5281/zenodo.11127272.

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"Revolutionizing Language Learning: Integration of AI to Personalized Language Learning" examines the profound influence of AI on language education. The article explores the fundamental nature of customized learning, emphasizing the importance of artificial intelligence in customizing learning experiences according to individual requirements. The text explores a range of AI-driven tools and methods used in language learning, with a particular focus on their effectiveness in improving learning results. The authors highlight the significance of AI in delivering instantaneous feedback, adaptable

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Torkhani, Dina. "AI-enhanced language learning." American Journal of STEM Education 11 (May 15, 2025): 69–82. https://doi.org/10.32674/232w1188.

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This study was conducted to investigate the effectiveness of Talkpal.AI in improving fluency, pronunciation, vocabulary, and grammar among 40 pre-intermediate English as a Foreign Language (EFL) students in Kuwait. The participants were informed of the research objectives and implementation. A pretest-posttest design was used within a quasi-experimental design. Statistical analysis showed that students made significant improvements in posttest fluency, pronunciation, vocabulary, and grammar (p < 0.05). While the study's limitations include sample size and lack of sample diversity, this find

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M. Vijayakumar and G. Chellapandiyan. "Language Learning Through AI Technology - LLA (Language Learning Apps)." International Research Journal on Advanced Engineering and Management (IRJAEM) 2, no. 04 (2024): 957–62. http://dx.doi.org/10.47392/irjaem.2024.0127.

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Learning is the soul of life. Every living being learns new things each and every day. Especially Language learning is a vital role from past to present generation. The modern world needs a skilled and more efficient people for handling different situations. Innovation was the only slogan for language experts.AI technology opens new window for those who are aspiring and developing language learning. Sounds and signs are the basic elements for language components. In the past, language learning was purely transmitted through oral method. After the peak of technological advancement teaching and

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Bajenescu, Titu-Marius I. "PROGRESS OF ARTIFICIAL INTELLIGENCE (AI)." Journal of Engineering Science XXV (2) (June 15, 2018): 57–64. https://doi.org/10.5281/zenodo.2564679.

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In the past few years, systems like Siri and Google Now opened our minds to the idea that we don’t have to be tethered to a laptop to have seamless interaction with information. In this model, AIs will move from speech recognition to natural language interaction, to natural language generation, and eventually to an ability to write as well as receive information. Machine learning is about man assisting computers. Deep learning is about systems beginning to progress and learn on their own. Historically, systems have always been trained, they’ve been programmed. And, over time, the p

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Rashad Seyidov. "Arabic Language Processing: Current Status And Future Prospects Of Artificial Intelligence." Journal of Namibian Studies : History Politics Culture 41 (April 25, 2024): 224–40. http://dx.doi.org/10.59670/jwszy037.

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This article provides an insightful exploration into the burgeoning field of Artificial Intelligence (AI) in the domain of Arabic language learning. With the rapid advancements in AI technologies, there has been a notable surge in interest and research focusing on its application within language education, particularly for less commonly taught languages like Arabic. This paper synthesizes the current state of AI in Arabic language learning, discussing various AI-driven approaches, tools, and applications employed in this domain. Furthermore, it highlights the challenges and opportunities encou

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Sarnovska, N., J. Rybinska, and Yu Mykhailichenko. "Enhancing university remote language learning through innovative applications of artificial intelligence technologies amidst global challenges." Teaching languages at higher institutions, no. 44 (May 31, 2024): 151–65. https://doi.org/10.26565/2073-4379-2024-44-10.

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In recent years, the integration of artificial intelligence (AI) technologies has revolutionised various industries, and education is no exception. One area where AI is making significant strides is in distance learning of foreign languages at the university level. The purpose of the article is to examine the many ways in which AI technologies can be used to improve the efficiency and effectiveness of foreign language learning in virtual classrooms based on a personalised approach to learning and to outline an algorithm for utilizing artificial intelligence in foreign language learning, which

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Aremu, Abdulahi Olarewaju. "Utilising AI-powered Chatbots for Learning Endangered Nigerian Languages and Considerations for Their Development." Journal of Communication, Language and Culture 4, no. 2 (2024): 41–56. http://dx.doi.org/10.33093/jclc.2024.4.2.3.

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Younger generations in Nigeria increasingly use English over their native languages, leading to a decline in indigenous language use in public and professional settings and risking language loss. Consequently, this study explores the potential of AI-powered chatbots in aiding the learning of endangered Nigerian languages. The objectives are to determine how chatbots can support language learning, identify the essential functionalities required for effective learning, and outline design considerations to address the specific challenges faced by these languages. The research employs a qualitativ

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Akinsemolu, Adenike A., and Helen Onyeaka. "The Role of Artificial Intelligence in Transforming Language Learning: Opportunities and Ethical Considerations." Journal of Language and Education 11, no. 1 (2025): 148–52. https://doi.org/10.17323/jle.2025.22118.

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Artificial Intelligence (AI) has transformed teaching and learning across various disciplines, among them languages. AI has numerous applications in language teaching and learning, including algorithms that personalize learning for individual learners, identify and structure lessons and learning activities based on a learner’s strengths, evaluate and provide constant feedback on a learner’s progress, and simulate interactive learning environments. Such innovative tools and applications enhance both language comprehension and production skills among learners, optimizing the outcomes of language

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S. Doğruer and Y. Tuğlu. "ARTIFICIAL INTELLIGENCE IN LANGUAGE EDUCATION IN TÜRKİYE: POTENTIAL CHALLENGES." TRAKIA JOURNAL OF SCIENCES 22, Supplement 1 (2025): 8. https://doi.org/10.15547/tjs.2024.s.01.013.

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This study examines the potential challenges that language learners might encounter during the process of AI-supported foreign language learning (English and German) in Türkiye. Despite the potential benefits of AI technologies in language learning, it is obvious that the use of these technologies also presents certain disadvantages and challenges. AIM: This study aims to address the potential challenges and adverse effects of AI-supported language learning applications, offering recommendations on how these issues can be overcome. METHOD: The study uses the document analysis method to examine

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Mohammed, Sahar Yousif, Mohammed Aljanabi, Alaa Mohammed Mahmood, and Isa AVCI. "Revolutionizing Language Learning: How AI Bots Enhance Language Acquisition." Babylonian Journal of Artificial Intelligence 2023 (September 11, 2023): 55–63. https://doi.org/10.58496/bjai/2023/009.

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Language learning has changed in recent years with the inclusion of Artificial Intelligence (AI) bots. This paper discusses how AI bots have changed language acquisition, paying more attention on how they improve language learning experience. The Article looks at what prominent AI bots such as Gemini, ChatGPT and Cloud can do to make personalized feedback a reality as well as enhance interactivity and convenience in language learning.This article examines the functionality of adaptive learning algorithms which correct errors in real time, along with immersive environments that demonstrate the

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Dissertations / Theses on the topic "AI in language learning"

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Shukla, Saurabh. "Development of a Human-AI Teaming Based Mobile Language Learning Solution for Dual Language Learners in Early and Special Educations." Wright State University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=wright1547127943126526.

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Santiago, Dionny. "A Model-Based AI-Driven Test Generation System." FIU Digital Commons, 2018. https://digitalcommons.fiu.edu/etd/3878.

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Achieving high software quality today involves manual analysis, test planning, documentation of testing strategy and test cases, and development of automated test scripts to support regression testing. This thesis is motivated by the opportunity to bridge the gap between current test automation and true test automation by investigating learning-based solutions to software testing. We present an approach that combines a trainable web component classifier, a test case description language, and a trainable test generation and execution system that can learn to generate new test cases. Training da

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Abi, kanaan Marianne. "AI-Enhanced Emergency Call Handling : Development of Multimodal Machine Learning Models." Electronic Thesis or Diss., Besançon, Université Marie et Louis Pasteur, 2025. http://www.theses.fr/2025PAST2003.

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Les centres d’appels d’urgence jouent un rôle essentiel dans la gestion des situations de crise, car ils sont les intervenants de première ligne qui facilitent l’intervention, déterminant souvent la différence entre la vie et la mort. Les progrès récents et rapides de l’intelligence artificielle (IA) ne peuvent être négligés, car ils ont ouvert de nouveaux horizons pour améliorer les capacités des services d’urgence. Avec des applications d’IA adéquates, il est possible d’aider les centres d’appels d’urgence à améliorer l’efficacité et la précision de la gestion des urgences, sauvant ainsi pot

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Lilja, Adam, and Max Kihlborg. "Important criteria when choosing a conversational AI platform for enterprises." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-280896.

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This paper evaluates and analyzes three conversational AI-platforms; Dialogflow (Google), Watson Assistant (IBM) and Teneo (Artificial Solutions) on how they perform based on a set of criteria; pricing model, ease-of-use, efficiency, experience working in the software and what results to expect from each platform. The main focus was to investigate the platforms in order to acquire an understanding of which platform would best be suited for enterprises. The platforms were compared by performing a variety of tasks aiming to answer these questions. The technical research was combined with an anal

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Wen, Tsung-Hsien. "Recurrent neural network language generation for dialogue systems." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/275648.

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Language is the principal medium for ideas, while dialogue is the most natural and effective way for humans to interact with and access information from machines. Natural language generation (NLG) is a critical component of spoken dialogue and it has a significant impact on usability and perceived quality. Many commonly used NLG systems employ rules and heuristics, which tend to generate inflexible and stylised responses without the natural variation of human language. However, the frequent repetition of identical output forms can quickly make dialogue become tedious for most real-world users.

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Acosta, Andrew D. "Laff-O-Tron: Laugh Prediction in TED Talks." DigitalCommons@CalPoly, 2016. https://digitalcommons.calpoly.edu/theses/1667.

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Did you hear where the thesis found its ancestors? They were in the "parent-thesis"! This joke, whether you laughed at it or not, contains a fascinating and mysterious quality: humor. Humor is something so incredibly human that if you squint, the two words can even look the same. As such, humor is not often considered something that computers can understand. But, that doesn't mean we won't try to teach it to them. In this thesis, we propose the system Laff-O-Tron to attempt to predict when the audience of a public speech would laugh by looking only at the text of the speech. To do this, we cre

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Viswavarapu, Lokesh Kumar. "Real-Time Finger Spelling American Sign Language Recognition Using Deep Convolutional Neural Networks." Thesis, University of North Texas, 2018. https://digital.library.unt.edu/ark:/67531/metadc1404616/.

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This thesis presents design and development of a gesture recognition system to recognize finger spelling American Sign Language hand gestures. We developed this solution using the latest deep learning technique called convolutional neural networks. This system uses blink detection to initiate the recognition process, Convex Hull-based hand segmentation with adaptive skin color filtering to segment hand region, and a convolutional neural network to perform gesture recognition. An ensemble of four convolutional neural networks are trained with a dataset of 25254 images for gesture recognition an

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Helwe, Chadi. "Evaluating and Improving the Reasoning Abilities of Language Models." Electronic Thesis or Diss., Institut polytechnique de Paris, 2024. http://www.theses.fr/2024IPPAT021.

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Cette thèse examine les capacités de raisonnement des Petits Modèles de Langage (SLMs) et Grands Modèles de Langage (LLMs) et expose leurs limites. Elle présente LogiTorch, une bibliothèque Python facilitant l’entraînement de modèles sur diverses tâches de raisonnement. La thèse inclut également TINA, une technique d’augmentation de données qui renforce la robustesse des SLMs face à la négation dans les tâches d’implication textuelle. De plus, la thèse explore les capacités des LLMs avec MAFALDA, un nouveau benchmark pour la classification des sophismes, intégrant une métrique d’évaluation qui

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Roos, Sofie. "Chatbots in education : A passing trend or a valuable pedagogical tool?" Thesis, Uppsala universitet, Institutionen för informatik och media, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-355054.

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Digitalizing education and reinventing the learning experience is one of the big challenges in this age of information. In the eld of E-learning, the application of a chatbot as part of the education has shown interesting potential, both as a teaching and administrative tool. Chatbots have been 'trending' for a few years and quite a few papers examining it in the educational sector has been published, albeit very little interest seems to have been given to the summation of this knowledge. In an attempt to fill the knowledge gap this thesis performed a literature study to examine the documented

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Baroncini, Gian Marco. "analisi dei principali campi del deep learning e delle loro reti neurali." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23368/.

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La tesi tratta una panoramica del campo innovativo del Deep Learning, analizzandone i principali campi di applicazione (Computer Vision e Natural Language Processing) per poi approfondirne nei dettagli più tecnici le reti neurali utilizzate. Si inizia la trattazione di questi modelli con le reti convoluzionali e le LSTM per poi descrivere strutture più elaborate e recenti che sono identificate come stato dell'arte di alcuni task fondamentali dell'intelligenza artificiale. Si è cercato di fornire in questo modo una visione più ampia delle tipologie di reti esistenti, evidenziando un’analisi

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Books on the topic "AI in language learning"

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Han, Soyeon Caren, Henry Weld, Yan Li, Jean Lee, and Josiah Poon. Natural Language Understanding in Conversational AI with Deep Learning. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-74364-1.

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A, Kaunzner Ulrike, ed. Pronunciation and the adult learner: Limitations and possibilities : atti del convegno e pubblicazioni collegate ai temi del convegno "Pronunciation and the adult learner, limitations and possibilities, Ausspracheverbesserung im Erwachsenenalter, Grenzen und Möglichkeiten," Forlì, 1997. CLUEB, 2000.

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Ruqaiya, Hasan, Martin J. R, and Halliday, M. A. K. 1925-, eds. Language development: Learning language, learning culture. Ablex Pub. Corp., 1989.

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Gass, Susan M., and Larry Selinker, eds. Language Transfer in Language Learning. John Benjamins Publishing Company, 1992. http://dx.doi.org/10.1075/lald.5.

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M, Gass Susan, and Selinker Larry 1937-, eds. Language transfer in language learning. J. Benjamins Pub. Co., 1994.

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M, Gass Susan, and Selinker Larry 1937-, eds. Language transfer in language learning. J. Benjamins Pub. Co., 1992.

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Vear, Craig, and Fabrizio Poltronieri, eds. The Language of Creative AI. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-10960-7.

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Enever, Janet, and Eva Lindgren, eds. Early Language Learning. Multilingual Matters, 2017. http://dx.doi.org/10.21832/9781783098323.

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Naremore, Rita C. Children learning language. 3rd ed. Harper & Row, 1990.

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Lan, Yu-Ju, and Scott Grant, eds. Contextual Language Learning. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3416-1.

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Book chapters on the topic "AI in language learning"

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Cope, Bill, Mary Kalantzis, and Gabriela C. Zapata. "Language Learning after Generative AI." In Generative AI Technologies, Multiliteracies, and Language Education. Routledge, 2025. https://doi.org/10.4324/9781003531685-2.

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Auger, Tom, and Emma Saroyan. "Building a Language Learning App." In Generative AI for Web Development. Apress, 2024. http://dx.doi.org/10.1007/979-8-8688-0885-2_9.

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Matthews, Blair. "Generative AI, Ethics, and Language Learning." In The Palgrave Encyclopedia of Computer-Assisted Language Learning. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-51447-0_94-1.

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Mujumdar, Aarati R. "AI in Teaching, Learning, and Research." In Teaching English as Second Language. Routledge India, 2025. https://doi.org/10.4324/9781003611301-11.

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Ohashi, Louise, and Antonie Alm. "Conversational AI Literacy." In The Palgrave Encyclopedia of Computer-Assisted Language Learning. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-51447-0_259-1.

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Xiao, Chaojun, Zhiyuan Liu, Yankai Lin, and Maosong Sun. "Legal Knowledge Representation Learning." In Representation Learning for Natural Language Processing. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1600-9_11.

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AbstractThe law guarantees the regular functioning of the nation and society. In recent years, legal artificial intelligence (legal AI), which aims to apply artificial intelligence techniques to perform legal tasks, has received significant attention. Legal AI can provide a handy reference and convenient legal services for legal professionals and non-specialists, thus benefiting real-world legal practice. Different from general open-domain tasks, legal tasks have a high demand for understanding and applying expert knowledge. Therefore, enhancing models with various legal knowledge is a key iss

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Lewandowska-Tomaszczyk, Barbara, and Sónia Sousa. "AI Trustworthiness and Persuasive Effects in Users’ Judgment." In Second Language Learning and Teaching. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-86158-1_16.

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Tschichold, Cornelia. "Educating Teachers About AI." In The Palgrave Encyclopedia of Computer-Assisted Language Learning. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-51447-0_269-1.

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Bédi, Branislav. "Generative AI in CALL." In The Palgrave Encyclopedia of Computer-Assisted Language Learning. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-51447-0_23-1.

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Darshan, N. A., S. Amruth, Aditya Ashok Shetty, T. N. Srujan Kumar, and D. Uma. "Chat2Fluency: Enhancing Language Learning Through Conversational AI." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2024. https://doi.org/10.1007/978-981-97-6992-6_7.

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Conference papers on the topic "AI in language learning"

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Istanti, Wati, Suseno, Santi Pratiwi, and Kundharu Saddhono. "AI-Driven Personalized Learning: Revolutionizing Language Education." In 2024 International Conference on IoT, Communication and Automation Technology (ICICAT). IEEE, 2024. https://doi.org/10.1109/icicat62666.2024.10923274.

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Gavriilidou, Zoe. "Content and AI Integrated Learning in Language Teaching for Enhanced AI Literacy." In 11th International Conference of the Immersive Learning Research Network. The Immersive Learning Research Network, 2025. https://doi.org/10.56198/nhe4nc11.

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Ntentos, Evangelos, Stephen John Warnett, and Uwe Zdun. "Rule-Based Assessment of Reinforcement Learning Practices Using Large Language Models." In 2025 IEEE/ACM 4th International Conference on AI Engineering – Software Engineering for AI (CAIN). IEEE, 2025. https://doi.org/10.1109/cain66642.2025.00009.

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Gowda, Dhanya D., Jagadish P, D. Pallavi, and Manasa Ediga. "AI-Based Learning Assistant Using Natural Language Processing." In 2025 International Conference on Computing for Sustainability and Intelligent Future (COMP-SIF). IEEE, 2025. https://doi.org/10.1109/comp-sif65618.2025.10969857.

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J, Adlin Jerusha, and R. Rajakumari. "AI and Machine Learning in Predictive Analytics for Language Learning Success." In 2024 5th International Conference on Data Intelligence and Cognitive Informatics (ICDICI). IEEE, 2024. https://doi.org/10.1109/icdici62993.2024.10810975.

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B, Sreela, P.Bindhu, M. Mythili, P. Vijaya Kumar, E. Pearlin, and Noor Kaylan Hamid. "Harnessing AI and Machine Learning to Transform English Language Learning Experiences." In 2025 International Conference on Emerging Smart Computing and Informatics (ESCI). IEEE, 2025. https://doi.org/10.1109/esci63694.2025.10988275.

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Unterweger, Marina, Corinna Hörmann, Lisa Kuka, and Barbara Sabitzer. "FROM CHALKBOARD TO CHATBOT: HOW AI IS CHANGING LANGUAGE EDUCATION." In 17th International Conference on Education and New Learning Technologies. IATED, 2025. https://doi.org/10.21125/edulearn.2025.0256.

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Tassi, Foteini, Venetia Tassi, and Maria Dangli. "EMERGING TECHNOLOGIES AND LANGUAGE LEARNING: LEVERAGING THE GEMINI CHATBOT AND AI TOOLS IN LANGUAGE SCHOOL COURSES." In 17th International Conference on Education and New Learning Technologies. IATED, 2025. https://doi.org/10.21125/edulearn.2025.0017.

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Senanayake, Savithree, Kasun Karunanayaka, and K. V. J. P. Ekanayake. "Review on AI Assistant Systems for Programming Language Learning in Learning Environments." In 2024 8th SLAAI International Conference on Artificial Intelligence (SLAAI-ICAI). IEEE, 2024. https://doi.org/10.1109/slaai-icai63667.2024.10844969.

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Lane, Nicholas. "The Future of Large Language Models (and AI) is Federated." In 2024 2nd International Conference on Federated Learning Technologies and Applications (FLTA). IEEE, 2024. https://doi.org/10.1109/flta63145.2024.10840007.

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Reports on the topic "AI in language learning"

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Arnold, Zachary, Joanne Boisson, Lorenzo Bongiovanni, Daniel Chou, Carrie Peelman, and Ilya Rahkovsky. Using Machine Learning to Fill Gaps in Chinese AI Market Data. Center for Security and Emerging Technology, 2021. http://dx.doi.org/10.51593/20200064.

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In this proof-of-concept project, CSET and Amplyfi Ltd. used machine learning models and Chinese-language web data to identify Chinese companies active in artificial intelligence. Most of these companies were not labeled or described as AI-related in two high-quality commercial datasets. The authors' findings show that using structured data alone—even from the best providers—will yield an incomplete picture of the Chinese AI landscape.

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Pasupuleti, Murali Krishna. AI-Driven Automation: Transforming Industry 5.0 withMachine Learning and Advanced Technologies. National Education Services, 2025. https://doi.org/10.62311/nesx/rr225.

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Abstract: This article delves into the transformative role of artificial intelligence (AI) and machine learning (ML) in shaping Industry 5.0, a paradigm centered on human- machine collaboration, sustainability, and resilient industrial ecosystems. Beginning with the evolution from Industry 4.0 to Industry 5.0, it examines core AI technologies, including predictive analytics, natural language processing, and computer vision, which drive advancements in manufacturing, quality control, and adaptive logistics. Key discussions include the integration of collaborative robots (cobots) that enhance hu

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Steel, Piers, and Hadi Fariborzi. Beyond ChatGPT: Using AI to Enhance Research Workflows. Instats Inc., 2025. https://doi.org/10.61700/57x0xi7z30bh11498.

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This one-day workshop equips PhD students and researchers with practical skills to integrate AI tools into their academic work effectively and ethically. Participants will gain hands-on experience with large language models, learning to enhance research quality and efficiency while navigating ethical considerations and leveraging AI for literature reviews, data analysis, and more.

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Marra de Artiñano, Ignacio, Franco Riottini Depetris, and Christian Volpe Martincus. Automatic Product Classification in International Trade: Machine Learning and Large Language Models. Inter-American Development Bank, 2023. http://dx.doi.org/10.18235/0005012.

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Accurately classifying products is essential in international trade. Virtually all countries categorize products into tariff lines using the Harmonized System (HS) nomenclature for both statistical and duty collection purposes. In this paper, we apply and assess several different algorithms to automatically classify products based on text descriptions. To do so, we use agricultural product descriptions from several public agencies, including customs authorities and the United States Department of Agriculture (USDA). We find that while traditional machine learning (ML) models tend to perform we

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Latorre, Lucia, Valentín Muro, Eduardo Rego, Mariana Gutierrez, Ignacio Cerrato, and Jose Daniel Zarate. Tech Report Artificial Intelligence. Inter-American Development Bank, 2024. http://dx.doi.org/10.18235/0013015.

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This report provides a comprehensive overview of AI, from its fundamentals to its practical applications, covering topics such as its definition, evolution, and implementation. It also delves into various applications, such as machine learning, natural language processing, computer vision, and generative AI, providing specific examples and use cases across sectors like healthcare, logistics, environment, and security.

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Korell, Johanna Lea, Subin Nijhawan, Roland Alexander Ißler, and Britta Viebrock. Fragebogen für Lernende "Künstliche Intelligenz und Fremdsprachen", im Rahmen des Artikels "Fremdsprachenlernen im Zeitalter Künstlicher Intelligenz – eine empirische Untersuchung zu Kenntnissen, Meinungen und Nutzungsweisen von Englisch-, Französisch- und Spanischschüler*innen der Sekundarstufen I und II". Universitätsbibliothek J. C. Senckenberg, Frankfurt am Main, 2025. https://doi.org/10.21248/gups.88393.

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This empirical study investigates AI knowledge, beliefs, and reported practices among secondary school learners of English, French, and Spanish in Germany (n=226). A survey revealed significant gaps between students' self-perceived and actual understanding of AI as well as their use and critical reflection on it. The findings suggest that integrating AI into foreign language learning, initially through targeted teacher training, is instrumental to develop both functional and evaluative skills among students, thereby sustainably fostering critical digital literacy.

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Mbani, Benson, Timm Schoening, and Jens Greinert. Automated and Integrated Seafloor Classification Workflow (AI-SCW). GEOMAR, 2023. http://dx.doi.org/10.3289/sw_2_2023.

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The Automated and Integrated Seafloor Classification Workflow (AI-SCW) is a semi-automated underwater image processing pipeline that has been customized for use in classifying the seafloor into semantic habitat categories. The current implementation has been tested against a sequence of underwater images collected by the Ocean Floor Observation System (OFOS), in the Clarion-Clipperton Zone of the Pacific Ocean. Despite this, the workflow could also be applied to images acquired by other platforms such as an Autonomous Underwater Vehicle (AUV), or Remotely Operated Vehicle (ROV). The modules in

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Goodwin, Sarah, Yigal Attali, Geoffrey LaFlair, et al. Duolingo English Test - Writing Construct. Duolingo, 2023. http://dx.doi.org/10.46999/arxn5612.

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Assessments, especially those used for high-stakes decision making, draw on evidence-based frameworks. Such frameworks inform every aspect of the testing process, from development to results reporting. The frameworks that language assessment professionals use draw on theory in language learning, assessment design, and measurement and psychometrics in order to provide underpinnings for the evaluation of language skills including speaking, writing, reading, and listening. This paper focuses on the construct, or underlying trait, of writing ability. The paper conceptualizes the writing construct

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Schwartz, Reva. The Language of Trustworthy AI:. National Institute of Standards and Technology, 2023. http://dx.doi.org/10.6028/nist.ai.100-3.

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Tan, Seng Chee. Learning from, learning with, learning about, and learning beyond AI. National Institute of Education, Nanyang Technological University, Singapore, 2024. https://doi.org/10.32658/10497/28845.

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