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1
Lombardi, Dario, Luigi Traetta, Antonio Maffei, and Primož Podžaj. "Evolving Educational Horizons: Integrating AI with Innovative Teaching and Assessment Strategies." EDUCATION SCIENCES AND SOCIETY, no. 2 (January 2025): 185–203. https://doi.org/10.3280/ess2-2024oa18462.
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This systematic review examines 39 studies to identify Teaching and Learning Activities (TLAs) and Assessment Tasks (ATs) aligned with Bloom's Taxonomy, highlighting their role in fostering critical thinking and creativity. TLAs such as simulations, problem-solving, and gamification, combined with peer assessments and formative feedback, support higher-order cognitive skills. However, the review reveals a critical gap in integrating AI into these frameworks, despite AI's potential to personalize learning and enhance assessments. This absence limits the development of adaptive learning environments that meet individual needs. Future research should prioritize AI-driven tools to create flexible and personalized educational pathways. Integrating AI into education is essential to promote higher-order thinking, improve instructional design, and address contemporary learning demands. By leveraging data-driven insights, AI could transform teaching practices and enhance student outcomes.
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Li, Manman, and Rumeng Duan. "Research Strategies and Practices of AI-Driven Secondary School English Teaching." Development of Humanities and Social Sciences 1, no. 1 (2025): 1000059. https://doi.org/10.71204/bnqn1w65.
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This study provides an in-depth analysis of the traditional challenges faced in secondary English education, including stereotypical teaching styles, lack of personalized education, lack of resources for teaching and research, and constraints on teachers’ professional growth. This paper proposes that the incorporation of Artificial Intelligence (AI) technology provides novel solutions to address these challenges. It enhances the interactivity and practicality of language learning through an intelligent context simulation system, customizes personalized learning paths using big data analysis, optimizes the allocation of teaching resources with the help of intelligent literature retrieval and analysis tools, and promotes teachers’ professional development through a teacher skills assessment and feedback mechanism. Looking ahead, the deep application of AI in education will bring more efficient, personalized and intelligent changes to secondary school English teaching.
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Mishra, Mr Siddhant. "Revolutionizing Education: The Impact of AI-Enhanced Teaching Strategies." International Journal for Research in Applied Science and Engineering Technology 12, no. 9 (2024): 9–32. http://dx.doi.org/10.22214/ijraset.2024.64127.
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In recent years, the integration of Artificial Intelligence (AI) into educational settings has revolutionized traditional teaching methodologies, leading to the development of innovative teaching strategies. This paper explores how AI-enhanced approaches are transforming education by personalizing learning experiences, improving student engagement, and optimizing instructional methods. Through adaptive learning platforms, AI tailors educational content to meet individual student needs, fostering a more inclusive and effective learning environment. Intelligent tutoring systems provide real-time feedback and support, enabling students to progress at their own pace and achieve mastery in various subjects. Additionally, AI-driven analytics offer educators valuable insights into student performance and learning patterns, facilitating data-informed decisionmaking and targeted interventions. By leveraging AI technologies, educators can create dynamic, interactive, and studentcentered classrooms that cater to diverse learning styles and preferences. This paper discusses the potential benefits and challenges of AI-enhanced teaching strategies and highlights the importance of integrating AI thoughtfully and ethically to maximize its positive impact on education.
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PRATAP SINGH, DIVYANSH. "Impact of Artificial Intelligence on the Academic Environment." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem44260.
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Integrating Artificial Intelligence (AI) in education has significantly transformed teaching methodologies, assessment systems, and student engagement. This study evaluates the impact of AIdriven personalized learning and compares AI-assisted grading systems with traditional methods. The research examines the relationship between AI adoption and student performance using a dataset of 115 respondents and applying statistical techniques such as correlation analysis, chi-square tests, and parabolic distribution modelling. Findings reveal that while AI contributes to improved learning outcomes, its influence on teaching preference remains statistically weak (correlation coefficient r = 0.089). Furthermore, chi-square analysis (χ² = 9.57, p = 0.144) suggests no significant association between AI performance perception and preference for AI-assisted teaching. The parabolic representation of mean (2.43), median (3.0), and mode (3.0) highlight a concentration of responses favoring AI-driven improvements, though variations exist across demographics. These results indicate that while AI enhances learning effectiveness, external factors such as subject complexity, institutional policies, and individual teaching styles may influence AI acceptance. The study contributes to future AI research by identifying key adoption trends, recommending data-driven strategies for AI integration in education, and addressing potential challenges such as algorithmic bias and accessibility gaps. The findings serve as a foundation for policymakers, educators, and researchers to refine AI-driven academic frameworks, ensuring equitable and efficient learning environments. Keywords: Artificial Intelligence, Personalized Learning, AI Grading, Statistical Analysis, Education Technology, Data-Driven Decision Making
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Adel, Mohammed Ahmed Abou, Moustafa Mohamed Abouelnour, Mohammad Issa Alhourani, and Asmaa A. Awad. "Towards Intelligent Universities Enhanced with Artificial Intelligence (AI)." Journal of Infrastructure, Policy and Development 9, no. 1 (2025): 10412. https://doi.org/10.24294/jipd10412.
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This paper presents a comprehensive and integrated paradigm for intelligent universities using artificial intelligence (AI) to transform management systems and teaching, thus complementing sustainable development objectives. Through a systematic examination of top worldwide universities’ AI applications, this study reveals key achievements, obstacles, and strategies for successfully implementing AI-driven intelligent universities. Every case study focuses on a particular AI-driven project, including the adaptive learning systems at MIT, the AI teaching assistant Jill Watson at Georgia Tech, and the AI-enabled quality control system at Cambridge University. Combining systematic review, meta-analysis, and case studies under a mixed-methods approach, the study provides a practical guide for implementing artificial intelligence to improve administrative and academic roles. Results show how artificial intelligence can solve institutional issues, automate quality assurance, and personalize learning. Recommendations advocate for gradual adoption strategies, ethical AI deployment, and capacity-building measures to enable sustainable digital transformation.
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Singh, Ajit Pal, Rahul Saxena, and Suyash Saxena. "The Future of Learning: AI-Driven Personalized Education." Asian Journal of Current Research 9, no. 4 (2024): 207–26. https://doi.org/10.56557/ajocr/2024/v9i49018.
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Virtual assistants are rapidly transforming the educational landscape. These AI-driven tools offer personalized support, enhancing student engagement and improving learning outcomes. Virtual assistants can answer questions instantly, clarify complex concepts, and create tailored study plans. By automating routine tasks and providing on-demand assistance, they free up educators to concentrate on more advanced instructional activities. Moreover, virtual assistants can analyze student performance data to pinpoint areas needing improvement and suggest targeted interventions. This data-driven approach allows educators to offer customized support and adjust their teaching strategies accordingly. While virtual assistants can't fully replace human interaction, they serve as valuable complements to traditional teaching methods, helping to foster a more dynamic and effective learning environment.
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Meng, Fanlong, and Wenxun Luo. "Artificial Intelligence and Teaching Strategies: A Comparative Study of Higher Education in China and the United States." Journal of Intelligence and Knowledge Engineering 2, no. 3 (2024): 107–12. https://doi.org/10.62517/jike.202404314.
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This study explores the integration of artificial intelligence in higher education teaching strategies in China and the United States, providing theoretical insights for global educational advancement. By systematically reviewing existing literature, the study identifies theoretical and practical pathways for embedding AI in these countries' educational systems. It examines the impact of AI on improving teaching strategies and educational quality. Utilizing qualitative research, policy analysis, and comparative methods, the research highlights specific differences and common challenges in AI's educational applications between the two nations. The analysis notes that the U.S. prioritizes personalized, data-driven teaching approaches due to its early adoption of AI, while China focuses on strategic investments to enhance educational quality and efficiency. Cultural distinctions and policy priorities influence how educational resources are allocated and teaching strategies are chosen, reflecting each country's strategic goals and educational values. The study concludes with recommendations for optimizing global teaching strategies through international collaboration and technology exchange. It emphasizes that AI in education is still in an exploratory stage, requiring ongoing assessment of its effectiveness and sustainability to foster a more innovative and inclusive teaching landscape.
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Guo, Yujie. "Teaching Model of Swimming Skills Training Under the Background of Artificial Intelligence." International Journal of Ambient Computing and Intelligence 15, no. 1 (2024): 1–18. http://dx.doi.org/10.4018/ijaci.356382.
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Global swimming competitiveness, driven by enhanced training science, necessitates innovative strategies. High-level swimmers worldwide follow common principles, underscoring AI's potential in training enhancement. Software engineering education actively seeks to align talent development with industry needs, particularly via AI-driven creation of immersive training environments for students. Despite UWB's prominence among wireless positioning technologies and ongoing Chinese research, its application in sports arenas is yet limited. This paper presents a graphical representation of AI in swimming training, proposing a 20% accuracy boost and data visualization, enriching discussions on AI-assisted learning platforms in software engineering and athletics.
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Adriana Rodríguez, Rosales. "The Impact of Statistics and Probability on Educational Artificial Intelligence." Advances and Applications in Statistics and Probability 1, no. 1 (2024): 001–4. http://dx.doi.org/10.17352/aasp.000001.
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Artificial intelligence has transformed e-learning by enabling personalized and efficient teaching. This manuscript analyzes the importance of statistics and probability in educational AI. Statistical methodologies improve decision-making, personalize learning, and optimize educational outcomes. Challenges such as data privacy and ethics are addressed. Case studies demonstrate the practical applications of AI in diverse educational contexts. Future directions suggest a need for robust research to further understand and implement AI-driven educational strategies. The findings underscore the critical role of data-driven approaches in shaping the future of education. Statistics and probability are not only foundational to the development of AI but also essential for analyzing vast amounts of educational data. They allow for the creation of predictive models that can identify student needs and adapt instructional methods accordingly. This adaptability enhances the learning experience by providing targeted support and resources to students, thereby improving their academic performance. Ethical considerations are fundamental when using AI to handle educational data. Protecting student data with privacy and security is crucial to maintaining trust in AI applications. This manuscript examines how educators and policymakers can collaborate to create guidelines that safeguard student information while utilizing data to enhance education. Integrating statistics and probability into educational AI significantly impacts and improves e-learning. Educators can enhance learning by employing data-driven strategies that provide personalized and effective teaching. This approach not only benefits individual learners but also contributes to the overall advancement of educational practices. Embracing these data-driven methodologies is essential for the continued evolution of teaching and learning in the digital age.
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Levitt, Greg, and Steven Grubaugh. "Mastering the Art of Teaching Secondary Students: Insights, Strategies, Research and AI-Driven Innovations." Technium Social Sciences Journal 70 (April 9, 2025): 171–76. https://doi.org/10.47577/tssj.v70i1.12636.
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Teaching secondary students requires a multifaceted set of skills that blend pedagogical theory, classroom management techniques, and a deep understanding of adolescent development. Drawing upon insights from veteran educators with several years of experience, this article examines the subtle and often underestimated nuances that contribute to effective teaching in a secondary setting. It highlights core practices that bolster classroom management, student engagement, and instructional differentiation. Moreover, the integration of artificial intelligence (AI) into teaching is explored, revealing how AI-driven tools can offer personalized solutions to classroom challenges. Anecdotes and practical examples illustrate how novice and early career teachers can bridge the gap between emerging instructional methods and the wisdom gleaned from years of hands-on experience. By embracing both tested, instinctive approaches and cutting-edge technologies, educators can foster dynamic and responsive learning environments that empower every student to achieve academic success.
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Shi, Hui, and Chan Fang. "Research on the Challenges and Resolution Pathways of College Public English Classroom Teaching in the Context of Artificial Intelligence." Industry Science and Engineering 1, no. 11 (2024): 56–63. https://doi.org/10.62381/i245b10.
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Integrating artificial intelligence (AI) into college English classroom teaching has introduced unprecedented opportunities while presenting significant challenges for public English education. The rapid advancement of AI technologies is reshaping teaching methodologies and fundamentally altering the educational ecosystem of college public English instruction. This study adopts a qualitative approach, conducting interviews with four college English teachers to examine the key challenges associated with AI-driven public English classrooms. The analysis focuses on five critical dimensions of English teaching: listening, speaking, reading, writing, and translation. By identifying these challenges and proposing practical resolution pathways, this research offers valuable theoretical insights and actionable strategies for optimizing the application of AI in college English teaching.
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Nieves, Charles Andre, Kyla Mae Lenantud, Nalodiya Dee Golingay, et al. "The Role of Artificial Intelligence in Shaping Effective Teaching Strategies: A Multiple Case Study of Filipino and Indonesian Teachers." Psychology and Education: A Multidisciplinary Journal 25, no. 7 (2024): 948–58. https://doi.org/10.5281/zenodo.13833996.
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Artificial Intelligence is considered a technological platform that impacts the teaching and learning process, particularly on its effect on assessment and grading and influence on graduates' future careers. However, the use of AI in the field of education, specifically on effective teaching strategies and classroom management poses issues experienced by teachers in the field. The increased use of AI in the classroom faces significant challenges because of insufficient resources for complete implementation. Nevertheless, researchers ground the argument in the gap and observed the need to find out more in the context of the experiences, coping mechanisms, and insights of Filipino and Indonesian teachers regarding the role of AI in shaping effective teaching strategies to improve student learning outcomes within the Philippine and Indonesian education system. Through this study initiative, an international university collaboration is established to conduct a multiple case study using qualitative interviews to gather essential information. Face-to-face interviews and online interviews were conducted with the participants with the guide of a given research questionnaire. Thus, semi-structured interviews are conducted with three Filipino and two Indonesian teachers as participants from private schools to gather their experiences in implementing and using AI-driven teaching strategies in their classrooms, forming the basis of this multiple case study. The researchers then proceeded to analyze the gathered information using descriptive-case analysis, within-case analysis, and cross-case analysis. In the concluding part of this study, from technical obstacles to systemic issues of digital infrastructure and technological inequality, these complexities underscore the need for concerted efforts to address barriers to AI-driven education. Despite these challenges, teachers demonstrate resilience and resourcefulness in navigating technological hurdles and promoting student engagement. By investing in professional development and fostering collaboration, educators unlock the full potential of AI in education and create inclusive learning environments that empower all students to succeed in the digital age.
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Adesh Kumar Pandey, Adesh Kumar Pandey, and Surendra Kumar Maurya Surendra Kumar Maurya. "Transforming Higher Education: The Role of Artificial Intelligence in Enhancing Learning and Teaching process." Journal of Research in Business and Management 13, no. 5 (2025): 99–103. https://doi.org/10.35629/3002-130599103.
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Artificial Intelligence (AI) in higher education has the potential to completely transforms the teaching and learning process while providing creative answers to persistent problems. This study examines how AI might improve higher education by adopting innovative ways like including data-driven decision-making, intelligent tutoring systems, personalised learning, and administrative efficiency. In order to improve student engagement and academic results, it explores AI-driven solutions like virtual assistants and adaptive learning platforms that may customize educational experiences to each individual student's needs. The study also examines the ramifications of AI for educators, including the ways it may affect professional development opportunities and instructional strategies. It illustrates the potential benefits and drawbacks of artificial intelligence (AI) in higher education through an extensive analysis of recent research and case studies. It ends with suggestions for incorporating AI technology in a way that maximizes their advantages while taking ethical and practical issues into account.
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Shukla, Harsh, and Kshama Pandey. "Human-AI Collaboration in Teaching and Learning." International Journal of Science and Social Science Research 2, no. 4 (2025): 367–75. https://doi.org/10.5281/zenodo.15107814.
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Artificial intelligence is transforming the educational landscape by enhancing instructional methodologies employed by educators and the processes through which students acquire knowledge. Artificial intelligence serves as a collaborative partner to educators by automating repetitive tasks, personalizing learning experiences, and facilitating cognitive development. This study explores the potential of AI-driven tools and pedagogical strategies to foster more inclusive, adaptive, and effective educational experiences. By reviewing existing literature and real-world applications, it highlights the benefits of AI-human collaboration, including improved teaching efficiency, better learning outcomes, and enhanced accessibility. The study further investigates critical challenges, including data privacy, bias, and the necessity for teacher training, underscoring the significance of ethical AI implementation. Looking ahead, the paper discusses future trends and the potential for wider AI adoption in education, ensuring technology serves as an enabler rather than a disruptor.
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Li, Yuxuan. "Study on the Role and Influence of AI in Personalized Learning for Second Language Acquisition." Journal of Education, Humanities and Social Sciences 38 (September 28, 2024): 125–31. http://dx.doi.org/10.54097/wfe3gk31.
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The combination of AI and second language acquisition can help to provide personalized teaching content and adjust learning more dynamically so that students can improve their learning efficiency and learning effect. Therefore, the use of AI in SLA has received widespread attention, but its accuracy and usability are still insufficient. This paper analyzes the particularity of SLA teaching and the application of AI in SLA, especially its potential to meet the unique needs of learners, optimize learning paths, and improve teaching efficiency. This paper concludes that the combination of teacher and AI is more ideal than the use of AI alone. Based on this, this paper proposes a new pedagogy that fully implements teacher-AI collaborative teaching in SLA. These improvements will promote a more effective language learning environment while strengthening the role of technology in supporting education, and these strategies will drive SLA teaching practices in a more personalized and technology-driven direction.
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Zhang, Binglin. "Research on AI-Empowered Precision Training Strategies for Rural Teachers." Education Insights 2, no. 6 (2025): 128–36. https://doi.org/10.70088/ms9sbr23.
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With the rapid development of information technology, artificial intelligence (AI) has become increasingly prevalent in education, yet rural teacher training still faces challenges such as insufficient resources, homogenized content, and difficulty matching training to individual needs. Focusing on AI-empowered precision training for rural teachers, this study constructs an indicator system grounded in precision-training theory, designs an intelligent training framework, and pilots a system prototype in model rural schools. First, we gathered teachers' needs and existing pain points through questionnaires and interviews, then applied machine-learning algorithms to develop multidimensional profiles of teaching ability, subject-matter knowledge, and professional aspirations. Based on these profiles, we used recommendation systems and intelligent instructional-analytics technologies to deliver customized courses and practical guidance. Finally, an empirical analysis compared experimental and control groups on teaching effectiveness, satisfaction, and professional-growth rates. Results indicate that introducing an AI-driven precision training mechanism significantly enhanced teaching ability, increased participation by 25%, improved course-match accuracy by 30%, and effectively supported teachers' ongoing professional development. Theoretical and practical optimization strategies and paths for broader adoption are proposed, offering reference for training-program innovators.
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De Gagne, Jennie C. "Values Clarification Exercises to Prepare Nursing Students for Artificial Intelligence Integration." International Journal of Environmental Research and Public Health 20, no. 14 (2023): 6409. http://dx.doi.org/10.3390/ijerph20146409.
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Artificial intelligence (AI) is rapidly revolutionizing health care and education globally, including nursing practice and education. The responsible utilization of AI in a nursing context requires thoughtful consideration of its alignment with nursing values such as compassionate and patient-centered care provision, and respect for diverse perspectives. Values clarification, a vital teaching strategy in nursing education, can reinforce the foundational values and beliefs that guide nursing practice, thereby facilitating nurses’ critical evaluation of the ethical implications of AI implementation. The early introduction of values clarification into nursing education (a) provides students with a framework to prioritize and reflect on the impact of nursing values on their practice, (b) enables educators to make informed decisions and enhance teaching strategies, (c) contributes to the continual improvement of nursing education programs, and (d) fosters an ethical and values-driven approach to the integration of AI into nursing education and practice. This article examines the integration of values clarification into nursing education, offers strategies for nurse educators to integrate AI into their teaching toolkit effectively and ethically, and addresses concerns regarding potential misuses of AI.
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Begum, Rahima. "A Systematic Literature Review on How Artificial Intelligence (AI) is Revolutionizing Education 4.0." Journal of Scientific Reports 9, no. 1 (2025): 60–79. https://doi.org/10.58970/jsr.1096.
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This paper assesses how artificial intelligence (AI) changes the education landscape, especially in Education 4.0. It also investigates how AI facilitates this change, improves educational methods, and identifies the main obstacles and future directions in its implementation. An educational transformation is underway to tackle the complex demands of today's learners to keep pace with the rapidly changing technological world. This transformation moves from conventional teaching methods towards embracing personalized learning experiences, AI-driven tutoring, streamlined administrative processes, and predictive analytics. Education 4.0 is a significant milestone where technology, particularly AI, is used to improve the educational journey. This article closely examines AI's function in education, emphasizing the benefits it provides on the learning experience and analyzing future trends in data-driven educational practices. The transition from traditional educational frameworks to Education 4.0 signifies a move away from rigid, uniform teaching methods in favor of more flexible and tailored educational approaches. Additionally, this study examines the idea of adaptive learning and how AI can modify teaching strategies instantly, taking into account feedback and the student's rate of progress. Lastly, it sheds light on the difficulties of broadly implementing AI in education.
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Gering, Zsuzsanna, Katalin Feher, Vanda Harmat, and Reka Tamassy. "Strategic organisational responses to generative AI-driven digital transformation in leading higher education institutions." International Journal of Organizational Analysis 33, no. 12 (2025): 132–52. https://doi.org/10.1108/ijoa-09-2024-4850.
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Purpose This study aims to explore generative artificial intelligence (AI) as a significant milestone and key driver of digital transformation in higher education, emphasising the urgent need for universities and policymakers to adapt strategies to remain effective, competitive and aligned with the rapidly evolving demands of education and research. Design/methodology/approach This study used qualitative content analysis to examine publicly available strategic documents and statements related to digital transformation from the top 30 ranked universities in the Times Higher Education 2024 Ranking, producing a data set of 98 strategies covering all key organisational domains. Findings The collected documents span eight areas, from teaching-learning strategies to information technology (IT) strategies and committees, with substantial variation among universities in scope, content and strategic combinations. A significant result is that teaching-learning offices and development centres serve as bridges between institutional strategies and grassroots innovation, absorbing top-down and bottom-up knowledge and fostering adaptive responses to generative AI-driven transformation. Practical implications By showcasing the best practices, this paper provides practical guidance for proactive institutional development, supporting university leadership in strategy-building and aiding national and international policymakers in shaping forward-looking frameworks. Originality/value Understanding and defining generative AI as a milestone in digital transformation is crucial for universities. Proactive adaptation to emerging trends and best practices enables institutions to navigate these challenges effectively.
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Lü, Xuanling. "A Quantitative Study of Artificial Intelligence in Foreign Language Talent Cultivation." English Language Teaching and Linguistics Studies 7, no. 2 (2025): p24. https://doi.org/10.22158/eltls.v7n2p24.
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The integration of artificial intelligence (AI) in foreign language education is reshaping traditional pedagogical models, offering new opportunities for personalized learning, adaptive assessments, and data-driven instructional strategies. This paper explores the impact of AI on foreign language talent cultivation through quantitative analysis of AI-assisted teaching methods and their effects on learner outcomes. By employing a large-scale survey and experimental design, the study evaluates how AI tools—such as intelligent tutoring systems, machine learning-based assessment models, and natural language processing tools—affect students’ language acquisition, engagement, and motivation. Results demonstrate that AI-enhanced learning models significantly improve vocabulary retention, speaking fluency, and overall learner satisfaction compared to traditional methods. This paper proposes a framework for AI-driven foreign language talent cultivation, emphasizing teacher training, curriculum redesign, and AI-supported learning environments.
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Zheng, Shen. "Artificial Intelligence – Driven Design of Aesthetic Education Curricula in Higher Education." Education Insights 2, no. 6 (2025): 247–56. https://doi.org/10.70088/ta9v8365.
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With the rapid advancement of artificial intelligence (AI) technologies, traditional university aesthetic education courses face challenges such as limited teaching modalities, inefficient resource utilization, and outdated assessment methods. Building on a comprehensive review of AI applications in education and classic theories of aesthetic education curricula, this paper proposes an "AI–Empowered Framework for University Aesthetic Education Curriculum Design", clarifying the central role of AI in course positioning, instructional objectives, and evaluation systems. Within this framework, we explore strategies for intelligent instructional content and resource design, AI–driven pedagogical innovations, and the development of intelligent platforms and creative evaluation tools for instructors and students. Through case studies in leading universities and empirical data analysis, we demonstrate that integrating AI into aesthetic education enhances students' artistic creativity, increases course engagement, and optimizes teaching management. Finally, we address challenges related to technology ethics, data security, and faculty development, and offer corresponding countermeasures and future research directions. This study enriches the theoretical system of aesthetic education Curriculum Design and provides actionable guidance for constructing an intelligent, aesthetic–centered learning ecosystem in the AI era.
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Chao, Zhu, An Dan, and Li Chengtao. "Intelligent Construction of Environmental Science Course with AI: Enhancing Teaching and Learning." Higher Education and Practice 2, no. 1 (2025): 21–22. https://doi.org/10.62381/h251104.
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This study delves into the intelligent construction of the "Introduction to Environmental Science" curriculum by leveraging AI technology and tools. Integrating cognitive theory, it endeavors to achieve differentiated teaching and assessment, thereby elevating the quality of teaching and learning. the paper elucidates the current application status of AI in environmental science education, its integration with cognitive theory, and the implementation of differentiated instructional strategies. A quantitative comparison predicting the enhancement of students' key competencies is presented. Moreover, data - driven evidence and practical cases are discussed, offering valuable insights for educational reform.
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Sayed Mahbub Hasan Amiri, Md Mainul Islam, Naznin Akter, Sk. Humaun Kabir, and Mohammad Shawkat Ali Mamun. "Effective teaching strategies: A deep dive into pedagogy." International Journal of Science and Research Archive 15, no. 1 (2025): 835–49. https://doi.org/10.30574/ijsra.2025.15.1.1055.
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This manuscript presents a deep exploration of effective teaching strategies, grounded in both theoretical foundations and practical implications, to address current educational needs. Drawing from constructivism’s experiential learning, Vygotsky’s sociocultural theory, behaviourist reinforcement, and humanistic, student-cantered approaches, the analysis identifies core strategies such as active learning (e.g., think-pair-share, problem-based learning), differentiated instruction, technology integration (e.g., flipped classrooms, AI tools), collaborative learning, and formative assessment. The discussion highlights these methods' proven impact on critical thinking, retention, and educational equity, supported by international case studies like Singapore’s differentiated math curriculum and contrasted with challenges such as technological disparities in rural areas. Systemic barriers including unequal resource distribution, institutional resistance to pedagogical change, and over-reliance on educational trends—are critically examined. The paper contrasts traditional lecture-based methods with contemporary practices that prioritize student agency. It advocates for flexible instructional designs, culturally responsive teaching, data-informed adjustments through learning analytics, and continuous improvement based on feedback. Emerging innovations such as AI-driven personalization, hybrid learning models, climate-focused education, Universal Design for Learning, and anti-bias curricula—are introduced as future directions, though the paper calls for more discussion on their prioritization. Concluding with a call to action, the manuscript urges educators to integrate innovation with evidence-based practice, adopt the role of facilitators, and create inclusive, adaptive learning environments. This work contributes to the literature by promoting a pedagogical evolution that not only empowers students to thrive in a complex world but also fosters a more equitable, ethical, and socially responsive education system.
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Hastomo, Tommy, Andini Septama Sari, Utami Widiati, Francisca Maria Ivone, Evynurul Laily Zen, and Andianto Andianto. "Exploring EFL Teachers' Strategies in Employing AI Chatbots in Writing Instruction to Enhance Student Engagement." World Journal of English Language 15, no. 7 (2025): 93. https://doi.org/10.5430/wjel.v15n7p93.
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Artificial Intelligence (AI) has become a powerful tool in English as a Foreign Language (EFL), offering significant prospects for improving language learning and teaching. Recently, the incorporation of chatbots, one of the advanced AI language models, in EFL writing has garnered interest. This study aims to investigate the use of AI chatbots in EFL writing instruction, driven by their potential to stimulate student engagement across affective, behavioral, and cognitive engagement. The main objective was to evaluate student engagement levels with AI chatbots and assess EFL teachers' strategies for stimulating this engagement. Utilizing a mixed-methods design, the research involved 40 students and two faculty members, employing questionnaires and semi-structured interviews for data collection. Quantitative data was analyzed using SPSS, and qualitative insights were obtained through thematic analysis of interview transcripts. Findings indicate that AI chatbots significantly improve student engagement, evidenced by high affective, behavioral, and cognitive engagement levels. The study identifies three effective strategies teachers use: personalized feedback, gamification, and interactive writing assignments. The research findings show the potential benefits of integrating AI chatbots into EFL writing instruction, facilitating informed decisions to optimize technology usage through understanding student engagement levels and effective teaching strategies, eventually enhancing student learning outcomes.
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Wei, Zhang, and Xie Shuzhen. "Transforming Immersion Education through AI-Driven Learning: A Descriptive Qualitative Study Investigation of Leadership Strategies for Scalable Program Development and Optimized Student Outcomes." Global Journal of Arts Humanity and Social Sciences 5, no. 2 (2025): 139–52. https://doi.org/10.5281/zenodo.14802561.
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Grounded in transformational leadership theory, the technology acceptance model, and constructivist learning theory, this descriptive qualitative research study explores principals' strategic leadership practices in optimizing student outcomes through AI-powered learning like ChatGPT in US-based Chinese immersion programs. We conducted in-depth interviews with 12 principals through purposive and snowball sampling to gain insights into their leadership strategies. We found three significant strategies for leaders to foster school success: employing data to enhance teaching practices, promoting a positive school culture to engage the community, and implementing research-based practices to support culturally responsive teaching. Additionally, we identified strategies for scalable program development and optimizing student outcomes, including achieving proficiency in language and core subjects, leveraging cultural exposure and cognitive development, and engaging students through culturally relevant content. This study fills in the gap in the literature by providing intersection insights of strategic leadership, AI-powered learning, and Chinese immersion education for student learning outcomes and school effectiveness. Findings offer significant implications for leaders, policymakers, scholars, and practitioners to leverage AI-powered learning in immersion education settings.
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Alamsyah, Nurwahyu, and Dale Neal. "Conceptualizing Artificial Intelligence in the Indonesian Education Systems and Reciprocity with AI-Based Curriculum." Internet of Things and Artificial Intelligence Journal 5, no. 1 (2025): 168–76. https://doi.org/10.31763/iota.v5i1.878.
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This study employs a Systematic Literature Review (SLR) to analyze the curriculum challenges in Indonesia and explore the potential AI-driven solutions to improve the education systems. The findings reveal that conventional teaching methods, lack of project-based learning (PBL), limited technology integration, and rigid learning approaches are among the most critical issues, leading to low student engagement and an imbalance between hard and soft skills. To address these challenges, AI technologies such as adaptive learning, formative assessments, and AI-powered virtual classrooms can be integrated to enhance personalized and competency-based learning. The findings suggest that by adopting AI-driven strategies, Indonesia can modernize its curriculum, enhance educational effectiveness, and align with global standards. This study contributes to evidence-based policymaking by offering insights into AI adoption in education and proposing a roadmap for a technology-integrated, student-centered learning system.
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Tang, Zhihao. "Using Artificial Intelligence in English Language Teaching: Benefits and Challenges." Journal of Education and Educational Research 12, no. 2 (2025): 1–4. https://doi.org/10.54097/e8f9jw03.
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The landscape of English Language Teaching (ELT) has undergone profound transformations in recent decades and it is driven by advancements in technology, evolving pedagogical approaches, and the increasing globalization of communication. English language continues to solidify its position as a global lingua franca. The rapid development of technology, especially the widespread use of computer networks, has brought unprecedented opportunities to ELT methods. Although Artificial Intelligence (AI) technologies have been increasingly integrated into ELT, there is a lack of comprehensive research that examines both the benefits and challenges of this integration from a global perspective. In this study, the author explores how AI technologies improve the effectiveness of ELT and the key challenges and limitations of integrating AI technologies into ELT. The research explores both the benefits and challenges of incorporating AI into the teaching process, highlighting innovative solutions for personalized learning, as well as concerns regarding the reliability and accuracy of AI. The study may emphasize the significance of maintaining a balance between AI technologies and human interaction. It could propose strategies to ensure that AI serves as a complement to, rather than a replacement for, traditional teaching methods and interpersonal communication within the classroom.
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Choi, Deahun. "Exploring Instructional Design Strategies for Utilizing Generative AI in Early Childhood Teacher Training Programs." K Association of Education Research 10, no. 1 (2025): 549–64. https://doi.org/10.48033/jss.10.1.25.
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This study aims to explore the instructional design approach utilizing generative AI (such as ChatGPT) in early childhood teacher training programs and examine its feasibility and applicability. The research was conducted through literature review, two rounds of expert focus group interviews (FGI), and the development of an instructional design model. The findings indicate that AI-based instructional design should be systematically structured, incorporating adaptive learning, AI-driven feedback and assessment, and intelligent learning assistants (chatbots). AI can serve as a tool to complement the role of instructors and support personalized learning. However, limitations such as the reliability of AI feedback and disparities in AI utilization skills also exist. To address these issues, it was concluded that maintaining the traditional role of instructors and adopting a hybrid assessment model that combines AI evaluation, peer assessment, and instructor evaluation is necessary. Based on the ADDIE instructional design model, a four-week AI-integrated instructional design framework was proposed, detailing AI-assisted collaborative learning and assessment methods. The study concludes that AI-based instructional design has the potential to improve teaching quality and enhance teachers' instructional planning skills in early childhood teacher training programs.
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Bivash, Mandal. "AI-Driven Discourse: Reimagining Pragmatics and Sociolinguistics in Digital English Communication." Literary Enigma 1, no. 2 (2025): 21–27. https://doi.org/10.5281/zenodo.15315713.
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Abstract The rapid integration of artificial intelligence (AI) into digital communication has transformed the English language’s pragmatic and sociolinguistic dimensions. This paper explores how AI-assisted tools, such as chatbots and virtual assistants, reshape discourse patterns, politeness strategies, and global English variations in online interactions. Drawing on computational linguistics and sociolinguistic frameworks, the study analyzes a corpus of AI-generated and humanAI hybrid conversations from social media platforms and educational contexts. Findings reveal that AI systems, while enhancing efficiency, often struggle to replicate nuanced pragmatic markers—such as implicature and politeness—leading to shifts in conversational norms. Additionally, the global proliferation of AI tools amplifies the dominance of standardized English, challenging regional dialects and linguistic diversity. The paper argues that these transformations necessitate a reevaluation of traditional linguistic theories to account for AI’s role as both a mediator and a creator of meaning. In the context of English Language Teaching (ELT), AI’s limitations in pragmatic competence highlight the need for hybrid pedagogies that blend technology with human interaction to foster cultural and linguistic sensitivity. Ethically, the study raises concerns about AI biases perpetuating hegemonic language practices and marginalizing non-standard English varieties. By bridging linguistics, AI, and pedagogy, this research contributes to the discourse on how digital tools redefine language use, identity, and education in an interconnected world. It aligns with the conference’s focus on the intersection of English language, AI, and cultural transformations. Keywords: Artificial intelligence (AI), Pragmatics, Sociolinguistics, English Language Teaching (ELT), Digital Communication.
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Manju G. Jhuriani. "A Study on Effectiveness of AI-Based Adaptive Learning Systems in Colleges in Thane District." Journal of Information Systems Engineering and Management 10, no. 50s (2025): 1056–62. https://doi.org/10.52783/jisem.v10i50s.10460.
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AI-powered adaptive learning platforms are transforming higher education by offering automated assessments, instant feedback, and personalized learning experiences. Tools like ChatGPT, Coursera, and AI-driven learning management systems enhance student engagement by tailoring content to individual needs while also assisting educators with lesson planning and performance tracking. Although these technologies are gaining traction in Thane District, widespread adoption is hindered by challenges such as inadequate faculty training, technological constraints, and ethical concerns.This study examines the perspectives, challenges, and overall satisfaction of faculty and students regarding the effectiveness of AI-driven adaptive learning systems in colleges across Thane District. Findings indicate that while only 40% of educators integrate AI into their teaching, 80% of students actively utilize AI tools. Despite this gap, 65% of faculty and 70% of students express satisfaction with AI-enhanced learning. Key barriers include technical constraints, ethical concerns, and insufficient faculty training. Additionally, skepticism among educators about AI’s reliability and long-term impact remains a concern. The study highlights the need for faculty training programs, institutional support, and structured AI implementation strategies to maximize the effectiveness of AI-driven learning in higher education.
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Mishra, Rashmi, and Deepika Varshney. "Academic Integrity in Higher Education: Faculty Perceptions, Strategies, and Digital Challenges in the Digital Age." International Journal of All Research Education and Scientific Methods (IJARESM) 12, no. 4 (2024): 1387–96. https://doi.org/10.56025/IJARESM.2023.1201241387.
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The study surveyed faculty across various disciplines and experience levels using a mixed-method approach, showing significant insights into their perceptions, methods, and challenges towards Academic Integrity. The findings indicate a high awareness of academic dishonesty, with most university students reportedly engaging in such acts. Faculty employ diverse strategies to combat academic dishonesty, including syllabus integrity expectations, ethical dialogues, and varied assessment formats. However, challenges like the influence of the digital environment, online learning issues, and student pressure persist. The study highlights the need for institutional policies and faculty development programs emphasizing ethical conduct and practical engagement in academic integrity. Recommendations include enhancing syllabus communication, engaging in ethical dialogues, and using varied assessment formats and teaching learning method along with the modification in pedagogies. The study aligns with Aristotle's virtue ethics, advocating for the development of moral virtues through practical engagements. Key findings highlight the role of Ethical Artificial Intelligence (AI) literacy in addressing Artificial Intelligence (AI)-driven academic dishonesty, suggesting the importance of Artificial Intelligence (AI) monitoring tools and Ethical Education. <strong>Keywords: Academic Integrity, Academic Dishonesty, Artificial Intelligence (AI), Digital Challenges, Ethical Conduct, Teaching Learning Methods, Virtue Ethics</strong>
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Bo, Zhou, Lim Seong Pek, Wang Cong, et al. "Transforming Translation Education: A Bibliometric Analysis of Artificial Intelligence’s Role in Fostering Sustainable Development." International Journal of Learning, Teaching and Educational Research 24, no. 3 (2025): 166–90. https://doi.org/10.26803/ijlter.24.3.9.
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This bibliometric analysis focused on the potential and difficulties of implementing artificial intelligence (AI) in translation education. This study aligns with Sustainable Development Goal 4 (Quality Education), whichemphasizes inclusive and equitable learning opportunities. It investigated the effects of AI tools on teaching methods, student engagement, and language skill development,including generative artificial intelligence (generative AI).Through co-citation and co-occurrence analysis of 281 Web of Science articles (2020–2024), this study identified key research trends, gaps, and interdisciplinary linkages. While AI research in education was extensive, its application in translation education remained fragmented and lacked a cohesive theoretical framework. This study extended AI adoption models by incorporating ethical considerations and pedagogical challenges, addressing gaps in prior research. The findings highlighted the need for institutional support, targeted training, and interdisciplinary cooperation to facilitate AI integration. This study identified gaps in AI-driven translation pedagogy and proposed a framework to enhance integration, particularly in teaching methodologies, ethics, and interdisciplinary collaboration. While AI fosters creativity in curriculum design, personalized learning, and multilingual communication, over-reliance on AI tools may weaken language proficiency. To address inequalities in AI access, inclusive and ethical AI integration strategies aligned with Sustainable Development Goal 10 (Reduced Inequalities) are crucial. This study reinforced the importance of institutional support, targeted training, and resource development to ensure sustainable AI adoption in translation education. It calls for informed policies and interdisciplinary cooperation to advance sustainable and equitable education while optimizing AI-driven learning environments.
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Glenny Jocelyn G. "The Impact of AI on Management Education." International Journal of Scientific Research in Science and Technology 12, no. 3 (2025): 1385–88. https://doi.org/10.32628/ijsrst25123152.
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Artificial Intelligence (AI) is revolutionizing management education by enhancing learning experiences, improving administrative efficiency, and providing data-driven decision-making capabilities. AI-powered tools, such as intelligent tutoring systems, machine learning algorithms, and virtual simulations, are reshaping traditional teaching methodologies. Recent studies indicate that AI-driven automation in education can improve learning outcomes by 30% while reducing administrative workload by 40%. AI enhances personalized learning, facilitates adaptive assessments, and supports real-time analytics for student performance tracking. However, challenges such as data privacy concerns, faculty resistance, and ethical implications remain barriers to full-scale AI adoption. This paper explores the impact of AI on management education, highlighting key benefits, challenges, and recent trends. It also discusses emerging technologies such as AI-powered virtual reality, peer-learning platforms, and blockchain-based credentialing. The study concludes that while AI holds immense potential to transform management education, institutions must address ethical concerns, provide faculty training, and ensure sustainable integration strategies. Future research should focus on developing inclusive AI frameworks that balance technology with traditional pedagogical methods to maximize learning outcomes and institutional efficiency.
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Polydoros, Georgios, Victoria Galitskaya, Pantelis Pergantis, Athanasios Drigas, Alexandros-Stamatios Antoniou, and Eleftheria Beazidou. "Innovative AI-Driven Approaches to Mitigate Math Anxiety and Enhance Resilience Among Students with Persistently Low Performance in Mathematics." Psychology International 7, no. 2 (2025): 46. https://doi.org/10.3390/psycholint7020046.
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This study explored innovative methods for teaching mathematics to seventh-grade students with persistently low performance by using an AI-driven neural network approach, specifically focusing on solving first-degree inequalities. Guided by the Response to Intervention (RTI) framework, the intervention aimed to reduce math anxiety and build academic resilience through the development of cognitive and metacognitive strategies. A rigorous pre- and post-test design was employed to evaluate changes in performance, anxiety levels, and resilience. Fifty-six students participated in the 12-week program, receiving personalized instruction tailored to their individual needs. The AI tool provided real-time feedback and adaptive problem-solving tasks, ensuring students worked at an appropriate level of challenge. Results indicated a marked decrease in math anxiety alongside significant gains in cognitive skills such as problem-solving and numerical reasoning. Students also demonstrated enhanced metacognitive abilities, including self-monitoring and goal setting. These improvements translated into higher academic performance, particularly in the area of inequalities, and greater resilience, highlighting the effectiveness of AI-based strategies in supporting learners who struggle persistently in mathematics. Overall, the findings underscore how AI-driven teaching approaches can address both the cognitive and emotional dimensions of mathematics learning. By offering targeted, adaptive support, educators can foster a learning environment that reduces stress, promotes engagement, and facilitates long-term academic success for students with persistently low performance in mathematics.
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Fil, Nataliia, Roman Slisarenko, Zhanna Deineko, and Lana Morozova. "Trends in Artificial Intelligence Research on Education: Topic Modeling Using Latent Dirichlet Allocation." Bulletin of Kharkov National Automobile and Highway University, no. 108 (May 16, 2025): 17. https://doi.org/10.30977/bul.2219-5548.2025.108.0.17.
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Abstract. Problem. The rapid advancement of artificial intelligence (AI) has significantly impacted various domains, particularly education. AI-driven tools are increasingly being integrated into teaching methodologies, assessment frameworks, and curriculum design. However, despite the growing research interest in Artificial Intelligence in Education (AIED), the field remains fragmented, lacking a comprehensive analysis of dominant research themes and emerging trends. Understanding these trends is crucial for optimizing AI applications in education and shaping future developments in the field. Goal. The primary goal of this study is to analyze the current landscape of AIED research by identifying key thematic clusters, evaluating dominant research directions, and predicting future trajectories in AI-driven education. Methodology. This study employs network analysis, topic modeling, and global research trend evaluation to analyze scientific publications in AIED. The Latent Dirichlet Allocation (LDA) algorithm is applied to classify research articles into thematic categories, enabling the identification of primary topics shaping the field. Data is sourced from the Web of Science database, and hierarchical clustering techniques are used to determine semantic similarities between thematic models. Results. The analysis reveals five major research clusters in AIED, including AI applications in medical education, machine learning in education, adaptive learning technologies, large language models in learning, and student-centered AI-driven educational strategies. The study identifies increasing trends in the adoption of generative AI and chatbot-based learning support, while traditional AI-driven assessment methodologies show moderate growth. The results also highlight strong interconnections between AI-driven personalization and intelligent tutoring systems, emphasizing the shift toward adaptive and student-centric learning environments. Originality. This research provides a structured and systematic analysis of AIED trends, leveraging advanced topic modeling techniques. Unlike previous studies focusing on isolated AI applications in education, this study presents a holistic view of thematic structures in AIED research, offering a quantitative and data-driven approach to understanding the evolution of the field. Practical Value. The findings can serve as a strategic reference for educators, policymakers, and AI developers in shaping future educational technologies. By identifying key thematic directions, this research supports evidence-based decision-making for integrating AI into educational systems. The results can also guide further research in AIED, helping scholars explore emerging AI-driven teaching methodologies, adaptive learning models, and ethical considerations in AI-powered education.
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Lyanda, Joanne Nabwire, Salmon Oliech Owidi, and Aggrey Mukasa Simiyu. "Rethinking Higher Education Teaching and Assessment In-Line with AI Innovations: A Systematic Review and Meta-Analysis." African Journal of Empirical Research 5, no. 3 (2024): 325–35. http://dx.doi.org/10.51867/ajernet.5.3.30.
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With the rapid advancement of artificial intelligence (AI) technologies, higher education institutions are increasingly exploring innovative ways to rethink teaching and assessment practices. This research paper examines the implications of AI on assessments in online learning environments. Specifically, the objectives of this study were to evaluate the effectiveness of AI-powered teaching methodologies in enhancing student engagement and learning outcomes in online education settings and, secondly, to analyze the impact of AI-driven assessment tools on the accuracy, reliability, and fairness of evaluating student performance in online learning environments through a systematic review and meta-analysis of existing literature. The study adopted activity theory to understand the issues around AI and assessment. The study adopted a mixed-methods design. The study adopted the use of meta-analysis in order to statistically combine results from multiple studies on a particular topic to provide a more comprehensive and reliable summary of the overall findings. The study found that to guarantee moral and just practices, there are issues with the integration of AI in online learning that need to be resolved. Key issues included data privacy, algorithmic prejudice, and the role of human instructors in the administration of the assessments online, carefully considered and addressed in a proactive manner. These findings provided insights on how AI can transform traditional teaching methods and assessment strategies, creating an AI-crowded environment that fosters student learning and academic success. Based on the findings, the study recommends that there is a need to integrate pedagogical strategies that leverage AI innovation, such as adaptive learning approaches, real-time feedback mechanisms, or interactive simulations, to improve teaching effectiveness and student performance in online settings.
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Hazaymeh, Wafa A., Abdeldjalil Bouzenoun, and Abdelghani Remache. "EFL Instructors’ Perspective on Using AI Applications in English as a Foreign Language Teaching and Learning." Emerging Science Journal 8 (March 12, 2024): 73–87. http://dx.doi.org/10.28991/esj-2024-sied1-05.
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This study aimed to explore the perspectives of EFL instructors working in a variety of universities in the UAE on the effectiveness of AI applications in the EFL classroom. EFL teachers need to use AI applications in ways that are aligned with instructional goals and support student learning. A quantitative approach was used, and data was gathered from a survey of 46 EFL instructors. The results showed that the instructors strongly relied on AI applications to facilitate tasks, offer data-driven insights to improve instructional strategies and customize the learning process for each student. They also positively valued the benefits that AI applications bring to their classrooms for improving the teaching process. Notably, the results showed that the years of teaching experience had a statistically significant impact on the means of EFL instructors' perspectives regarding the benefits of adopting AI apps in EFL classrooms. The results also showed that, despite teaching experience, there were no significant differences in perceptions regarding the challenges of utilizing AI apps. This is probably because EFL students are accustomed to using technology in their lectures. Due to their benefits in English language instruction, the study suggests incorporating AI applications into the EFL teaching process. Doi: 10.28991/ESJ-2024-SIED1-05 Full Text: PDF
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Joanne, Nabwire Lyanda, and Oliech Owidi Salmon. "Integrating Artificial Intelligence in Micro Teaching: The Role of ChatGPT for Customized Feedback and Interactive Learning." International Journal of Recent Research in Social Sciences and Humanities (IJRRSSH) 12, no. 2 (2025): 1–10. https://doi.org/10.5281/zenodo.15130275.
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<strong>Abstract:</strong><em> </em>The integration of Artificial Intelligence (AI) in teaching has transformed conventional teacher training methods, offering AI-driven feedback systems, interactive simulations, and adaptive learning environments. This study explored the role of AI, particularly generative models like ChatGPT, in enhancing lesson delivery, instructional feedback, and teacher engagement in micro- teaching. AI-powered platforms provide real-time, systematic, and personalized feedback, analyzing verbal communication, lesson structuring, and classroom engagement techniques to improve teaching effectiveness. Additionally, AI-driven simulations enable pre-service teachers to practice classroom management, respond to diverse learning scenarios, and develop adaptive instructional strategies in a risk-free virtual environment. Despite these advancements, AI in micro-teaching presents significant challenges, including bias in AI-generated feedback, lack of emotional intelligence, data privacy concerns, and the potential over-reliance on automation. Research highlights that while AI offers consistency and efficiency, it lacks the depth of human evaluation, particularly in assessing creativity, socialization, student engagement, and emotional responsiveness. A hybrid feedback model that integrates AI-driven analytics with human mentoring is recommended to balance structured feedback with contextual and personalized insights. This literature review synthesizes theoretical frameworks, such as Constructivist Learning Theory, Feedback and Learning Theories, and the Artificial Intelligence in Education (AIED) Framework, to explain AI’s role in micro-teaching. Findings suggest that AI-enhanced micro-teaching can complement conservative evaluation methods, leading to a more engaging, individualized, and efficient teacher training experience. However, ethical considerations and responsible AI integration must be prioritized to ensure fair, unbiased, and effective use of AI in education. This study contributes to the ongoing discourse on AI’s impact in teacher education, offering insights into its potential, limitations, and future directions. <strong>Keywords:</strong> micro-teaching, Artificial intelligence, AI-generated feedback, AI-powered simulations, Hybrid AI-human evaluation. <strong>Title:</strong> Integrating Artificial Intelligence in Micro Teaching: The Role of ChatGPT for Customized Feedback and Interactive Learning <strong>Author:</strong> Joanne Nabwire Lyanda, Salmon Oliech Owidi <strong>International Journal of Recent Research in Social Sciences and Humanities (IJRRSSH)</strong> <strong>ISSN 2349-7831</strong> <strong>Vol. 12, Issue 2, April 2025 - June 2025</strong> <strong>Page No: 1-10</strong> <strong>Paper Publications </strong> <strong>Website: www.paperpublications.org</strong> <strong>Published Date: 04-April-2025</strong> <strong>DOI: https://doi.org/10.5281/zenodo.15130275</strong> <strong>Paper Download Link (Source)</strong> <strong>https://www.paperpublications.org/upload/book/Integrating%20Artificial%20Intelligence-04042025-3.pdf</strong>
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Chang, Daniel H., Michael Pin-Chuan Lin, Shiva Hajian, and Quincy Q. Wang. "Educational Design Principles of Using AI Chatbot That Supports Self-Regulated Learning in Education: Goal Setting, Feedback, and Personalization." Sustainability 15, no. 17 (2023): 12921. http://dx.doi.org/10.3390/su151712921.
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The invention of ChatGPT and generative AI technologies presents educators with significant challenges, as concerns arise regarding students potentially exploiting these tools unethically, misrepresenting their work, or gaining academic merits without active participation in the learning process. To effectively navigate this shift, it is crucial to embrace AI as a contemporary educational trend and establish pedagogical principles for properly utilizing emerging technologies like ChatGPT to promote self-regulation. Rather than suppressing AI-driven tools, educators should foster collaborations among stakeholders, including educators, instructional designers, AI researchers, and developers. This paper proposes three key pedagogical principles for integrating AI chatbots in classrooms, informed by Zimmerman’s Self-Regulated Learning (SRL) framework and Judgment of Learning (JOL). We argue that the current conceptualization of AI chatbots in education is inadequate, so we advocate for the incorporation of goal setting (prompting), self-assessment and feedback, and personalization as three essential educational principles. First, we propose that teaching prompting is important for developing students’ SRL. Second, configuring reverse prompting in the AI chatbot’s capability will help to guide students’ SRL and monitoring for understanding. Third, developing a data-driven mechanism that enables an AI chatbot to provide learning analytics helps learners to reflect on learning and develop SRL strategies. By bringing in Zimmerman’s SRL framework with JOL, we aim to provide educators with guidelines for implementing AI in teaching and learning contexts, with a focus on promoting students’ self-regulation in higher education through AI-assisted pedagogy and instructional design.
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Zahra, Samer, Medhat Samra, and Lamis El Gizawi. "Working Toward Advanced Architectural Education: Developing an AI-Based Model to Improve Emotional Intelligence in Education." Buildings 15, no. 3 (2025): 356. https://doi.org/10.3390/buildings15030356.
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This study explored the integration of emotional intelligence (EI) with artificial intelligence (AI) to address emerging challenges in architectural education. An AI-supported teaching model was developed, utilizing AI tools to assess students’ emotional responses and enabling educators to adapt teaching strategies based on emotional data. This study employed a three-phase methodology: theoretical, analytical, and experimental phases. The theoretical phase involved a comprehensive literature review focusing on the role of EI in architectural education. In the analytical phase, a survey was conducted to evaluate students’ ability to overcome learning challenges using a case study from an Egyptian university. The experimental phase implemented an EI-driven teaching approach with a pilot group of students, incorporating instructor feedback and ChatGPT-4O for assessments in order to minimize potential bias. The results demonstrate that integrating EI into education significantly enhances students’ performance compared to traditional teaching methods. Furthermore, the findings contribute to the development of an AI-based model that provides personalized feedback and fosters a dynamic learning environment, aiming to achieve higher academic and behavioral standards among architecture students. This research offers theoretical and practical insights into advancing the integration of AI and EI in architectural education.
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Supadi Supadi. "Implementation of Data-Based Management in Improving the Effectiveness of AI-Assisted Learning in Senior High School." Jurnal Ilmiah Rumpun Ilmu Pendidikan 1, no. 4 (2024): 61–82. https://doi.org/10.63760/jirip.v1i4.31.
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This study aims to analyze the implementation of data-based management in improving the effectiveness of AI-assisted learning in Senior High School. The unit of analysis in this study is a school that implements a data-based management system in AI-assisted learning. The research design used is a qualitative study with a case study approach. The research data sources consist of school principals, teachers, and students involved in the implementation of AI-assisted learning. Data collection techniques include in-depth interviews, participatory observation, and analysis of school documents. The data obtained was analyzed using thematic analysis techniques to identify patterns and relationships between concepts. Key findings show that the implementation of data-driven management can improve learning personalization, optimization of teaching strategies, and the effectiveness of the use of AI in the learning process. The main contribution of this research is to provide empirical insights into the role of data-driven management in supporting AI-assisted learning innovations at the secondary education level.
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Rejoice Elikem Vorsah and Frank Oppong. "Leveraging AI to enhance active learning strategies in science classrooms: implications for teacher professional development." World Journal of Advanced Research and Reviews 24, no. 2 (2024): 1355–70. http://dx.doi.org/10.30574/wjarr.2024.24.2.3499.
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The integration of artificial intelligence (AI) in educational settings offers transformative potential for enhancing active learning strategies in science classrooms. This paper explores how AI-driven tools can support the implementation of active learning methodologies such as problem-based learning (PBL), interactive simulations, and personalized learning pathways. These strategies have been shown to increase student engagement, foster critical thinking, and deepen the understanding of scientific concepts. The analysis highlights the role of AI in creating adaptive learning environments where students receive real-time feedback and differentiated instruction tailored to their individual learning needs. An essential aspect of leveraging AI for active learning is ensuring that teachers are adequately prepared to implement these technologies effectively. The discussion delves into professional development programs that equip educators with the skills and knowledge to incorporate AI tools into their teaching practices. Such programs should emphasize hands-on training, collaborative workshops, and continuous learning opportunities that align with current advancements in educational technology. By fostering teacher confidence and proficiency, these initiatives ensure that educators can maximize the benefits of AI to enhance student learning outcomes. The paper also considers the implications of adopting AI in teaching for long-term educational practices, including ethical considerations, data privacy concerns, and the importance of maintaining a human-centric approach in classrooms. Examples of successful implementations and case studies provide insights into best practices and the challenges encountered. This comprehensive approach underscores the value of combining innovative technology with strategic teacher development to create enriched, interactive, and sustainable learning environments that promote critical thinking and environmental awareness among students.
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Rejoice, Elikem Vorsah, and Oppong Frank. "Leveraging AI to enhance active learning strategies in science classrooms: implications for teacher professional development." World Journal of Advanced Research and Reviews 24, no. 2 (2024): 1355–70. https://doi.org/10.5281/zenodo.15094299.
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The integration of artificial intelligence (AI) in educational settings offers transformative potential for enhancing active learning strategies in science classrooms. This paper explores how AI-driven tools can support the implementation of active learning methodologies such as problem-based learning (PBL), interactive simulations, and personalized learning pathways. These strategies have been shown to increase student engagement, foster critical thinking, and deepen the understanding of scientific concepts. The analysis highlights the role of AI in creating adaptive learning environments where students receive real-time feedback and differentiated instruction tailored to their individual learning needs. An essential aspect of leveraging AI for active learning is ensuring that teachers are adequately prepared to implement these technologies effectively. The discussion delves into professional development programs that equip educators with the skills and knowledge to incorporate AI tools into their teaching practices. Such programs should emphasize hands-on training, collaborative workshops, and continuous learning opportunities that align with current advancements in educational technology. By fostering teacher confidence and proficiency, these initiatives ensure that educators can maximize the benefits of AI to enhance student learning outcomes. The paper also considers the implications of adopting AI in teaching for long-term educational practices, including ethical considerations, data privacy concerns, and the importance of maintaining a human-centric approach in classrooms. Examples of successful implementations and case studies provide insights into best practices and the challenges encountered. This comprehensive approach underscores the value of combining innovative technology with strategic teacher development to create enriched, interactive, and sustainable learning environments that promote critical thinking and environmental awareness among students.
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Zhang, Guangyong. "Transformer-based AI framework for optimising English teaching evaluation strategies: a data-driven and explainable approach." International Journal of Information and Communication Technology 26, no. 9 (2025): 107–27. https://doi.org/10.1504/ijict.2025.145828.
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Miltenoff, Plamen. "THE IMPACT OF DIGITIZATION: FOSTERING MEDIA, NEWS, DIGITAL, CRITICAL DATA, AND AI LITERACIES." LAW AND THE BUSINESS IN THE CONTEMPORARY SOCIETY 1, no. 1 (2024): 336–47. https://doi.org/10.56065/lbcs/2024.336.
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This paper examines the challenges and opportunities in fostering AI literacy within Bulgaria and the broader Central and Eastern European (CEE) region. It emphasizes the importance of foundational literacies—media, news, digital, and critical data—as critical enablers for developing comprehensive AI literacy. The paper highlights key initiatives, including workshops at the University of Economics – Varna, AI-enhanced teaching tools like ChatGPT, and microcredentialing programs aimed at preparing students for AI-driven job markets. Despite these efforts, challenges such as institutional resistance, limited funding, and language barriers persist. Recommendations for coordinated strategies to address these gaps and build sustainable frameworks for AI literacy education are shared, aiming for an effective engagement by academia and society with the opportunities and challenges of an AI-integrated future.
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Yang, Yang. "Innovative Teaching Modes and Multimedia Integration in University Piano Education." Highlights in Art and Design 9, no. 2 (2025): 29–33. https://doi.org/10.54097/zs1jsm41.
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This paper explores the integration of innovative teaching modes and multimedia technology in university piano education. With rapid advancements in digital tools, traditional piano teaching methods are evolving to incorporate technology-driven pedagogical strategies. This study examines innovative teaching methods such as flipped classrooms, blended learning, and artificial intelligence (AI)-assisted instruction. Additionally, it assesses the effectiveness of multimedia integration, including the use of digital pianos, virtual learning environments, and interactive applications. Through data analysis and case studies, this research highlights the benefits, challenges, and future directions of multimedia-enhanced piano instruction in higher education.
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Coffin Murray, Meg. "Bridging the Generative AI Literacy Gap: A Guide to Introducing Prompt Engineering in University Courses." Issues in Informing Science and Information Technology 22 (2025): 010. https://doi.org/10.28945/5516.
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Aim/Purpose To address the gap in students’ effective use of generative AI tools, this paper presents a framework to introduce university students to the principles and practices of prompt engineering – the art and science of crafting precise and purposeful inputs to guide LLMs in generating accurate and useful outputs. This paper aims to equip students with strategies to interact meaningfully with AI chatbots for academic success. Background Generative AI tools, like ChatGPT, are widely adopted in educational settings, yet many students lack the skills to harness their full potential. This paper introduces prompt engineering as a critical competency for students to develop both technical proficiency and critical thinking. Methodology The paper provides a structured framework for teaching prompt engineering in university courses. It draws on existing literature, practical applications, and pedagogical strategies to guide educators in integrating generative AI effectively into their university courses. Contribution This paper contributes to the body of knowledge by presenting a comprehensive framework for teaching prompt engineering. It highlights prompt engineering’s role in enhancing AI literacy and preparing students for technology-driven academic and professional environments. Findings Prompt engineering enhances students’ ability to generate precise and relevant outputs from AI tools by supporting student development of communication strategies tailored to large language models. This guide introduces essential concepts and skills that facilitate effective interaction with AI chatbots. Structured instruction in prompt engineering helps to foster critical thinking, problem-solving, and reflective interaction – key competencies for navigating an AI-driven environment. Additionally, integrating prompt engineering into education improves AI literacy, enabling students to tackle complex tasks and apply AI tools effectively across various disciplines. Recommendations for Practitioners Educators should integrate structured, prompt engineering instruction into their courses, emphasizing its interdisciplinary applications. Scaffolded learning will help students develop competency in applying prompt engineering techniques and strategies. Recommendations for Researchers Future studies should explore the long-term impact of prompt engineering instruction on academic performance and professional readiness. Additionally, research should examine its effectiveness across diverse disciplines. Impact on Society Teaching prompt engineering equips students with essential AI literacy skills, fostering responsible and innovative use of AI in academic, professional, and societal contexts. This contributes to a workforce better prepared for the challenges of the AI era. Future Research Further research should examine the integration of multimodal AI tools alongside prompt engineering to assess how combined approaches can enhance learning outcomes. In addition, studies should investigate the effective-ness of various instructional designs to identify best practices for promoting student engagement and skill development. Exploring discipline-specific and pedagogically meaningful student use cases will also be essential to guiding the thoughtful integration of AI tools across diverse educational contexts.
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Mr., Anup D. Mohadkar, and Mrs. S. T. Aurangabadkar Dr. "Artificial Intelligence and Work-Life Balance: Transforming the Academic Landscape for Educators." International Journal of Advance and Applied Research S6, no. 22 (2025): 419–24. https://doi.org/10.5281/zenodo.15502012.
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<em>The rapid integration of Artificial Intelligence (AI) within academia has generated considerable interest in its potential to transform educators' work-life balance, teaching methodologies, and institutional practices. This study examines the role of AI in reshaping the academic landscape, specifically focusing on its effects on workload distribution, job expectations, and mental well-being. The research illuminates how AI can streamline administrative tasks, enhance student engagement, and personalize learning experiences by exploring the benefits and challenges associated with AI-driven automation. However, significant ethical concerns—such as data privacy, algorithmic bias, and the possible devaluation of human expertise—remain critical challenges. The study also assesses institutional policies and strategies that facilitate AI adoption while promoting a healthy work-life balance for educators. Based on the findings, recommendations are provided to harness AI effectively, ensuring that it increases productivity without undermining educators' well-being. This research relies on secondary data and emphasizes the necessity for a balanced approach to AI integration, highlighting the importance of collaboration, ethical frameworks, and human-centric teaching practices.</em>
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Mr., Anup D. Mohadkar, and Mrs. S. T. Aurangabadkar Dr. "Artificial Intelligence and Work-Life Balance: Transforming the Academic Landscape for Educators." International Journal of Advance and Applied Research S6, no. 22 (2025): 832–37. https://doi.org/10.5281/zenodo.15533490.
Full textAbstract:
<em>The rapid integration of Artificial Intelligence (AI) within academia has generated considerable interest in its potential to transform educators' work-life balance, teaching methodologies, and institutional practices. This study examines the role of AI in reshaping the academic landscape, specifically focusing on its effects on workload distribution, job expectations, and mental well-being. The research illuminates how AI can streamline administrative tasks, enhance student engagement, and personalize learning experiences by exploring the benefits and challenges associated with AI-driven automation. However, significant ethical concerns—such as data privacy, algorithmic bias, and the possible devaluation of human expertise—remain critical challenges. The study also assesses institutional policies and strategies that facilitate AI adoption while promoting a healthy work-life balance for educators. Based on the findings, recommendations are provided to harness AI effectively, ensuring that it increases productivity without undermining educators' well-being. This research relies on secondary data and emphasizes the necessity for a balanced approach to AI integration, highlighting the importance of collaboration, ethical frameworks, and human-centric teaching practices.</em>
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Nyamwange, Callen. "Assessing the Extent of Integration of Artificial Intelligence in Teaching and Learning at Kenyan Universities." Pan-African Journal of Education and Social Sciences 6, no. 1 (2025): 76–87. https://doi.org/10.56893/pajes2025v06i01.06.
Full textAbstract:
Artificial Intelligence (AI) in university teaching is reshaping higher education by enhancing instructional methods, student engagement, and learning outcomes. This study explores the use of AI-driven tools, personalized learning, and adaptive instructional strategies. It establishes the motivation and satisfaction levels in adopting AI over traditional methods in university teaching experience. The study used the Technological Pedagogical Content Knowledge (TPACK) theory (Mishra & Koehler, 2006) and Diffusion of Innovation (DOI) theory (Rogers, 2003) to underscore the role of technology in fostering continuous learning and interaction. Data were collected from students across diverse programs through the Learning Management Systems (LMS) and analyzed using AI-powered learning analytics and natural language processing (NLP) tools. Key metrics included course completion rates, assignment engagement, and assessment performance. The findings reveal that AI facilitates content personalization and real-time feedback and improves student participation, ultimately optimizing the learning experience. This study provides valuable insights into student interactions, preferences, and effectiveness of AI in higher education. It also highlights the need for universities to strategically utilize AI to foster inclusive, adaptive, and efficient teaching environments. The research recommends embracing AI as a transformative tool to maximize its potential in shaping the future of university education in Kenya.