ChatGPT in Classrooms: Opportunities, Challenges, and Ethical Considerations

Author: (1) Mohammad AL-Smad, Qatar University, Qatar and (e-mail: malsmadi@qu.edu.qa). Table of Links Abstract and Introduction History of Using AI in Education Research Methodology Literature Review Summary Conclusion and References 6. Conclusion Using ChatGPT and other generative AI tools in education offers several benefits. It allows for a more personalized and efficient learning experience for students, as the technology can adapt to individual needs and provide tailored support. Additionally, it enables teachers to deliver feedback more quickly and easily, enhancing the learning process. ChatGPT plays several roles in education, including providing information, facilitating debates and discussions, supporting selfdirected learning, and creating content for course materials. In response to a specific prompt, ChatGPT can generate cases for learning specific topics. However, there are also challenges to consider. The effectiveness of the technology in educational settings is still largely untested, and there may be limitations in the quality of data that AI chatbots rely on. Ethical considerations, such as privacy and bias, as well as safety concerns, must also be addressed when implementing ChatGPT or similar tools in education. By addressing the challenges posed by AI technologies and leveraging their advantages, a fair and effective education system that provides individualized teaching, feedback, and support can be built. This survey sheds light on the relationship between ChatGPT and teachers, revealing the different roles that each entity can play in the educational context. It emphasizes the importance of teachers’ adapted pedagogical expertise while using such technology and highlights the potential usage of generative AI models to enhance instructional practices. As a pioneering effort, this survey emphasized the need for future research to provide deeper insights into the application of generative AI models in teaching and learning. It also emphasized the importance of making appropriate pedagogical adjustments to effectively integrate these models into instruction. Moreover, this study highlights the need for a collaboration among educators, researchers, and policy-makers to develop regulatory guidelines and practices that ensure the ethical and responsible use of generative AI models in education. References Adiguzel, T., Kaya, M. H., & Cansu, F. K. (2023). Revolutionizing education with ai: Exploring the transformative potential of chatgpt. Contemporary Educational Technology, 15, ep429 Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big?. In Proceedings of the 2021 ACM conference on fairness, accountability, and transparency (pp. 610–623). Bitzer, D., Braunfeld, P., & Lichtenberger, W. (1961). Plato: An automatic teaching device. IRE Transactions on Education, 4, 157–161. Block, J. H., & Burns, R. B. (1976). Mastery learning. Review of research in education, 4, 3–49. Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E. et al. (2021). On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258, Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A. et al. (2020). Language models are few-shot learners. Advances in neural information processing systems, 33, 1877–1901. Chan, C. K. Y. (2023). A comprehensive ai policy education framework for university teaching and learning. International Journal of Educational Technology in Higher Education, 20, 1–25 Chaudhry, I. S., Sarwary, S. A. M., Refae, G. A. E., & Chabchoub, H. (2023). Time to revisit existing student’s performance evaluation approach in higher education sector in a new era of chatgpt — a case study. Cogent Education, 10, 2210461. URL: https://doi.org/10.1080/2331186X.2023.2210461. doi:10.1080/2331186X.2023. 2210461. arXiv:https://doi.org/10.1080/2331186X.2023.2210461. Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264–75278. doi:10.1109/ACCESS.2020.2988510. Choi, J. H., Hickman, K. E., Monahan, A., & Schwarcz, D. (2023). Chatgpt goes to law school. Journal of Legal Education (Forthcoming), . Choudhury, A., & Shamszare, H. (2023). Investigating the impact of user trust on the adoption and use of chatgpt: Survey analysis. Journal of Medical Internet Research, 25, e47184. Christiano, P. F., Leike, J., Brown, T., Martic, M., Legg, S., & Amodei, D. (2017). Deep reinforcement learning from human preferences. Advances in neural information processing systems, 30. Clow, D. (2013). An overview of learning analytics. Teaching in Higher Education, 18, 683–695. Cooper, G. (2023). Examining science education in chatgpt: An exploratory study of generative artificial intelligence. Journal of Science Education and Technology, 32, 444–452 Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2023). Chatting and cheating: Ensuring academic integrity in the era of chatgpt. Innovations in Education and Teaching International, 0, 1– 12. URL: https://doi.org/10.1080/14703297.2023.2190148. doi:10.1080/14703297.2023.2190148. arXiv:https://doi.org/10.1080/14703297.2023.2190148. Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, . Dosilovi ˇ c, F. K., Br ´ ciˇ c, M., & Hlupi ´ c, N. (2018). Explainable artificial intelligence: A survey. In ´ 2018 41st International convention on information and communication technology, electronics and microelectronics (MIPRO) (pp. 0210–0215). IEEE. Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., Albanna, H., Albashrawi, M. A., Al-Busaidi, A. S., Balakrishnan, J., Barlette, Y., Basu, S., Bose, I., Brooks, L., Buhalis, D., Carter, L., Chowdhury, S., Crick, T., Cunningham, S. W., Davies, G. H., Davison, R. M., De, R., Dennehy, D., Duan, Y., Dubey, R., Dwivedi, R., Edwards, J. S., Flavi ´ an, C., Gauld, ´ R., Grover, V., Hu, M.-C., Janssen, M., Jones, P., Junglas, I., Khorana, S., Kraus, S., Larsen, K. R., Latreille, P., Laumer, S., Malik, F. T., Mardani, A., Mariani, M., Mithas, S., Mogaji, E., Nord, J. H., O’Connor, S., Okumus, F., Pagani, M., Pandey, N., Papagiannidis, S., Pappas, I. O., Pathak, N., Pries-Heje, J., Raman, R., Rana, N. P., Rehm, S.-V., Ribeiro-Navarrete, S., Richter, A., Rowe, F., Sarker, S., Stahl, B. C., Tiwari, M. K., van der Aalst, W., Venkatesh, V., Viglia, G., Wade, M., Walton, P., Wirtz, J., & Wright, R. (2023). Opinion paper: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management, 71, 102642. URL: https://www.sciencedirect.com/science/article/pii/S0268401223000233. doi:https://doi.org/10.1016/j.ijinfomgt.2023.102642. Enqvist, L. (2023). ‘human oversight’in the eu artificial intelligence act: what, when and by whom? Law, Innovation and Technology, (pp. 1–28). Eysenbach, G. et al. (2023). The role of chatgpt, generative language models, and artificial intelligence in medical education: a conversation with chatgpt and a call for papers. JMIR Medical Education, 9, e46885. Farrokhnia, M., Banihashem, S. K., Noroozi, O., & Wals, A. (2023). A swot analysis of chatgpt: Implications for educational practice and research. Innovations in Education and Teaching International, (pp. 1–15). Fergus, S., Botha, M., & Ostovar, M. (2023). Evaluating academic answers generated using chatgpt. Journal of Chemical Education, 100, 1672–1675. Foroughi, B., Senali, M. G., Iranmanesh, M., Khanfar, A., Ghobakhloo, M., Annamalai, N., & Naghmeh-Abbaspour, B. (2023). Determinants of intention to use chatgpt for educational purposes: Findings from pls-sem and fsqca. International Journal of Human–Computer Interaction, (pp. 1–20). Gibson, D., Kovanovic, V., Ifenthaler, D., Dexter, S., & Feng, S. (2023). Learning theories for artificial intelligence promoting learning processes. British Journal of Educational Technology, n/a. URL: https://bera-journals. onlinelibrary.wiley.com/doi/abs/10.1111/bjet.13341. doi:https://doi.org/10.1111/bjet. 13341. arXiv:https://bera-journals.onlinelibrary.wiley.com/doi/pdf/10.1111/bjet.13341. Glaser, N. (2023). Exploring the potential of chatgpt as an educational technology: An emerging technology report. Technology, Knowledge and Learning, (pp. 1–8). Guo, K., Zhong, Y., Li, D., & Chu, S. K. W. (2023). Effects of chatbot-assisted in-class debates on students’ argumentation skills and task motivation. Computers and Education, 203, 104862. URL: https://www.sciencedirect.com/science/article/pii/S0360131523001392. doi:https://doi.org/ 10.1016/j.compedu.2023.104862. Hollingsworth, J. (1960). Automatic graders for programming classes. Communications of the ACM, 3, 528–529. Iku-Silan, A., Hwang, G.-J., & Chen, C.-H. (2023). Decision-guided chatbots and cognitive styles in interdisciplinary learning. Computers and Education, 201, 104812. URL: https://www.sciencedirect.com/science/ article/pii/S0360131523000891. doi:https://doi.org/10.1016/j.compedu.2023.104812. Jeon, J., & Lee, S. (2023). Large language models in education: A focus on the complementary relationship between human teachers and chatgpt. Education and Information Technologies, (pp. 1–20). Kasneci, E., Sessler, K., Kuchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., G ¨ unnemann, ¨ S., Hullermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfe ¨ ffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., Stadler, M., Weller, J., Kuhn, J., & Kasneci, G. (2023). Chatgpt for good? on opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. URL: https://www.sciencedirect.com/science/article/pii/S1041608023000195. doi:https://doi.org/ 10.1016/j.lindif.2023.102274. Keng-Boon Ooi, M. A.-E. M. A. A.-S. A. C. A. C. Y. K. D. T.-L. H. A. K. K. V.-H. L. X.-M. L. A. M. P. M. E. M. N. P. R. R. N. P. R. P. S. A. S. C.-I. T. S. F. W., Garry Wei-Han Tan, & Wong, L.-W. (2023). The potential of generative artificial intelligence across disciplines: Perspectives and future directions. Journal of Computer Information Systems, 0, 1–32. URL: https://doi.org/10.1080/08874417.2023.2261010. doi:10.1080/08874417.2023.2261010. arXiv:https://doi.org/10.1080/08874417.2023.2261010. Knobloch, K., Yoon, U., & Vogt, P. M. (2011). Preferred reporting items for systematic reviews and meta-analyses (prisma) statement and publication bias. Journal of Cranio-Maxillofacial Surgery, 39, 91–92. URL: https:// www.sciencedirect.com/science/article/pii/S1010518210002180. doi:https://doi.org/10.1016/ j.jcms.2010.11.001. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521, 436–444. Lodge, J. M., Thompson, K., & Corrin, L. (2023). Mapping out a research agenda for generative artificial intelligence in tertiary education. Australasian Journal of Educational Technology, 39, 1–8. URL: https://ajet.org.au/ index.php/AJET/article/view/8695. doi:10.14742/ajet.8695. Madiega, T. (2021). Artificial intelligence act. European Parliament: European Parliamentary Research Service, . McGee, R. W. (2023). Using chatgpt to conduct literature searches: A case study. Journal of Business Ethics, 95, 165–178. Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems, 26. Mohamed, A. M. (2023). Exploring the potential of an ai-based chatbot (chatgpt) in enhancing english as a foreign language (efl) teaching: perceptions of efl faculty members. Education and Information Technologies, (pp. 1–23). Mohammed, M., Kumar, N., Zawiah, M., Al-Ashwal, F. Y., Bala, A. A., Lawal, B. K., Wada, A. S., Halboup, A., Muhammad, S., Ahmad, R. et al. (2023). Psychometric properties and assessment of knowledge, attitude, and practice towards chatgpt in pharmacy practice and education: a study protocol. Journal of Racial and Ethnic Health Disparities, (pp. 1–10). Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & Group*, P. (2009). Preferred reporting items for systematic reviews and meta-analyses: the prisma statement. Annals of internal medicine, 151, 264–269. Neelakantan, A., Xu, T., Puri, R., Radford, A., Han, J. M., Tworek, J., Yuan, Q., Tezak, N., Kim, J. W., Hallacy, C. et al. (2022). Text and code embeddings by contrastive pre-training. arXiv preprint arXiv:2201.10005, . Nickolls, J., & Dally, W. J. (2010). The gpu computing era. IEEE micro, 30, 56–69. Radford, A., Narasimhan, K., Salimans, T., Sutskever, I. et al. (2018). Improving language understanding by generative pre-training, . Reiser, R. A. (2001a). A history of instructional design and technology: Part i: A history of instructional media. Educational technology research and development, 49, 53–64. Reiser, R. A. (2001b). A history of instructional design and technology: Part ii: A history of instructional design. Educational technology research and development, 49, 57–67. Russell, S. J., & Norvig, P. (2010). Artificial intelligence a modern approach. London. Schuett, J. (2023). Risk management in the artificial intelligence act. European Journal of Risk Regulation, (pp. 1–19). Shoufan, A. (2023). Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access, 11, 38805–38818. doi:10.1109/ACCESS.2023.3268224. Smith, A., Hachen, S., Schleifer, R., Bhugra, D., Buadze, A., & Liebrenz, M. (2023). Old dog, new tricks? exploring the potential functionalities of chatgpt in supporting educational methods in social psychiatry. International Journal of Social Psychiatry, (p. 00207640231178451). Stetten, K. J. (1971). Ticcit: A delivery system designed for mass utilization., . Stojanov, A. (2023). Learning with chatgpt 3.5 as a more knowledgeable other: an autoethnographic study. International Journal of Educational Technology in Higher Education, 20, 35. Strzelecki, A. (2023). To use or not to use chatgpt in higher education? a study of students’ acceptance and use of technology. Interactive Learning Environments, 0, 1–14. URL: https://doi.org/10.1080/10494820.2023.2209881. doi:10.1080/10494820.2023.2209881. arXiv:https://doi.org/10.1080/10494820.2023.2209881 Su, J., & Yang, W. (2023). Unlocking the power of chatgpt: A framework for applying generative ai in education. ECNU Review of Education, (p. 20965311231168423). Susarla, A., Gopal, R., Thatcher, J. B., & Sarker, S. (2023). The janus effect of generative ai: Charting the path for responsible conduct of scholarly activities in information systems. Information Systems Research, . Sutskever, I., Vinyals, O., & Le, Q. V. (2014). Sequence to sequence learning with neural networks. Advances in neural information processing systems, 27. Tlili, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T., Huang, R., & Agyemang, B. (2023). What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments, 10, 15 Tsai, M.-L., Ong, C. W., & Chen, C.-L. (2023). Exploring the use of large language models (llms) in chemical engineering education: Building core course problem models with chat-gpt. Education for Chemical Engineers, 44, 71–95. URL: https://www.sciencedirect.com/science/article/pii/S1749772823000180. doi:https://doi.org/10.1016/j.ece.2023.05.001. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30 Wardat, Y., Tashtoush, M. A., AlAli, R., & Jarrah, A. M. (2023). Chatgpt: A revolutionary tool for teaching and learning mathematics. Eurasia Journal of Mathematics, Science and Technology Education, 19, em2286. Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E., Le, Q. V., Zhou, D. et al. (2022). Chain-of-thought prompting elicits reasoning in large language models. Advances in Neural Information Processing Systems, 35, 24824–24837. Wu, R., & Yu, Z. (2023). Do ai chatbots improve students learning outcomes? evidence from a meta-analysis. British Journal of Educational Technology, n/a. URL: https://bera-journals. onlinelibrary.wiley.com/doi/abs/10.1111/bjet.13334. doi:https://doi.org/10.1111/bjet. 13334. arXiv:https://bera-journals.onlinelibrary.wiley.com/doi/pdf/10.1111/bjet.13334. Wu, X., Zhu, X., Wu, G.-Q., & Ding, W. (2013). Data mining with big data. IEEE transactions on knowledge and data engineering, 26, 97–107. Yan, D. (2023). Impact of chatgpt on learners in a l2 writing practicum: An exploratory investigation. Education and Information Technologies, (pp. 1–25). This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Author: (1) Mohammad AL-Smad, Qatar University, Qatar and (e-mail: malsmadi@qu.edu.qa). Author: Author: (1) Mohammad AL-Smad, Qatar University, Qatar and (e-mail: malsmadi@qu.edu.qa). Table of Links Abstract and Introduction Abstract and Introduction History of Using AI in Education History of Using AI in Education Research Methodology Research Methodology Literature Review Literature Review Summary Summary Conclusion and References Conclusion and References 6. Conclusion Using ChatGPT and other generative AI tools in education offers several benefits. It allows for a more personalized and efficient learning experience for students, as the technology can adapt to individual needs and provide tailored support. Additionally, it enables teachers to deliver feedback more quickly and easily, enhancing the learning process. ChatGPT plays several roles in education, including providing information, facilitating debates and discussions, supporting selfdirected learning, and creating content for course materials. In response to a specific prompt, ChatGPT can generate cases for learning specific topics. However, there are also challenges to consider. The effectiveness of the technology in educational settings is still largely untested, and there may be limitations in the quality of data that AI chatbots rely on. Ethical considerations, such as privacy and bias, as well as safety concerns, must also be addressed when implementing ChatGPT or similar tools in education. By addressing the challenges posed by AI technologies and leveraging their advantages, a fair and effective education system that provides individualized teaching, feedback, and support can be built. This survey sheds light on the relationship between ChatGPT and teachers, revealing the different roles that each entity can play in the educational context. It emphasizes the importance of teachers’ adapted pedagogical expertise while using such technology and highlights the potential usage of generative AI models to enhance instructional practices. As a pioneering effort, this survey emphasized the need for future research to provide deeper insights into the application of generative AI models in teaching and learning. It also emphasized the importance of making appropriate pedagogical adjustments to effectively integrate these models into instruction. Moreover, this study highlights the need for a collaboration among educators, researchers, and policy-makers to develop regulatory guidelines and practices that ensure the ethical and responsible use of generative AI models in education. References Adiguzel, T., Kaya, M. H., & Cansu, F. K. (2023). Revolutionizing education with ai: Exploring the transformative potential of chatgpt. Contemporary Educational Technology, 15, ep429 Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big?. In Proceedings of the 2021 ACM conference on fairness, accountability, and transparency (pp. 610–623). Bitzer, D., Braunfeld, P., & Lichtenberger, W. (1961). Plato: An automatic teaching device. IRE Transactions on Education, 4, 157–161. Block, J. H., & Burns, R. B. (1976). Mastery learning. Review of research in education, 4, 3–49. Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E. et al. (2021). On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258, Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A. et al. (2020). Language models are few-shot learners. Advances in neural information processing systems, 33, 1877–1901. Chan, C. K. Y. (2023). A comprehensive ai policy education framework for university teaching and learning. International Journal of Educational Technology in Higher Education, 20, 1–25 Chaudhry, I. S., Sarwary, S. A. M., Refae, G. A. E., & Chabchoub, H. (2023). Time to revisit existing student’s performance evaluation approach in higher education sector in a new era of chatgpt — a case study. Cogent Education, 10, 2210461. URL: https://doi.org/10.1080/2331186X.2023.2210461. doi:10.1080/2331186X.2023. 2210461. arXiv:https://doi.org/10.1080/2331186X.2023.2210461. Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264–75278. doi:10.1109/ACCESS.2020.2988510. Choi, J. H., Hickman, K. E., Monahan, A., & Schwarcz, D. (2023). Chatgpt goes to law school. Journal of Legal Education (Forthcoming), . Choudhury, A., & Shamszare, H. (2023). Investigating the impact of user trust on the adoption and use of chatgpt: Survey analysis. Journal of Medical Internet Research, 25, e47184. Christiano, P. F., Leike, J., Brown, T., Martic, M., Legg, S., & Amodei, D. (2017). Deep reinforcement learning from human preferences. Advances in neural information processing systems, 30. Clow, D. (2013). An overview of learning analytics. Teaching in Higher Education, 18, 683–695. Cooper, G. (2023). Examining science education in chatgpt: An exploratory study of generative artificial intelligence. Journal of Science Education and Technology, 32, 444–452 Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2023). Chatting and cheating: Ensuring academic integrity in the era of chatgpt. Innovations in Education and Teaching International, 0, 1– 12. URL: https://doi.org/10.1080/14703297.2023.2190148. doi:10.1080/14703297.2023.2190148. arXiv:https://doi.org/10.1080/14703297.2023.2190148. Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, . Dosilovi ˇ c, F. K., Br ´ ciˇ c, M., & Hlupi ´ c, N. (2018). Explainable artificial intelligence: A survey. In ´ 2018 41st International convention on information and communication technology, electronics and microelectronics (MIPRO) (pp. 0210–0215). IEEE. Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., Albanna, H., Albashrawi, M. A., Al-Busaidi, A. S., Balakrishnan, J., Barlette, Y., Basu, S., Bose, I., Brooks, L., Buhalis, D., Carter, L., Chowdhury, S., Crick, T., Cunningham, S. W., Davies, G. H., Davison, R. M., De, R., Dennehy, D., Duan, Y., Dubey, R., Dwivedi, R., Edwards, J. S., Flavi ´ an, C., Gauld, ´ R., Grover, V., Hu, M.-C., Janssen, M., Jones, P., Junglas, I., Khorana, S., Kraus, S., Larsen, K. R., Latreille, P., Laumer, S., Malik, F. T., Mardani, A., Mariani, M., Mithas, S., Mogaji, E., Nord, J. H., O’Connor, S., Okumus, F., Pagani, M., Pandey, N., Papagiannidis, S., Pappas, I. O., Pathak, N., Pries-Heje, J., Raman, R., Rana, N. P., Rehm, S.-V., Ribeiro-Navarrete, S., Richter, A., Rowe, F., Sarker, S., Stahl, B. C., Tiwari, M. K., van der Aalst, W., Venkatesh, V., Viglia, G., Wade, M., Walton, P., Wirtz, J., & Wright, R. (2023). Opinion paper: “so what if chatgpt wrote it?” multidisciplinary perspectives on opportunities, challenges and implications of generative conversational ai for research, practice and policy. International Journal of Information Management, 71, 102642. URL: https://www.sciencedirect.com/science/article/pii/S0268401223000233. doi:https://doi.org/10.1016/j.ijinfomgt.2023.102642. Enqvist, L. (2023). ‘human oversight’in the eu artificial intelligence act: what, when and by whom? Law, Innovation and Technology, (pp. 1–28). Eysenbach, G. et al. (2023). The role of chatgpt, generative language models, and artificial intelligence in medical education: a conversation with chatgpt and a call for papers. JMIR Medical Education, 9, e46885. Farrokhnia, M., Banihashem, S. K., Noroozi, O., & Wals, A. (2023). A swot analysis of chatgpt: Implications for educational practice and research. Innovations in Education and Teaching International, (pp. 1–15). Fergus, S., Botha, M., & Ostovar, M. (2023). Evaluating academic answers generated using chatgpt. Journal of Chemical Education, 100, 1672–1675. Foroughi, B., Senali, M. G., Iranmanesh, M., Khanfar, A., Ghobakhloo, M., Annamalai, N., & Naghmeh-Abbaspour, B. (2023). Determinants of intention to use chatgpt for educational purposes: Findings from pls-sem and fsqca. International Journal of Human–Computer Interaction, (pp. 1–20). Gibson, D., Kovanovic, V., Ifenthaler, D., Dexter, S., & Feng, S. (2023). Learning theories for artificial intelligence promoting learning processes. British Journal of Educational Technology, n/a. URL: https://bera-journals. onlinelibrary.wiley.com/doi/abs/10.1111/bjet.13341. doi:https://doi.org/10.1111/bjet. 13341. arXiv:https://bera-journals.onlinelibrary.wiley.com/doi/pdf/10.1111/bjet.13341. Glaser, N. (2023). Exploring the potential of chatgpt as an educational technology: An emerging technology report. Technology, Knowledge and Learning, (pp. 1–8). Guo, K., Zhong, Y., Li, D., & Chu, S. K. W. (2023). Effects of chatbot-assisted in-class debates on students’ argumentation skills and task motivation. Computers and Education, 203, 104862. URL: https://www.sciencedirect.com/science/article/pii/S0360131523001392. doi:https://doi.org/ 10.1016/j.compedu.2023.104862. Hollingsworth, J. (1960). Automatic graders for programming classes. Communications of the ACM, 3, 528–529. Iku-Silan, A., Hwang, G.-J., & Chen, C.-H. (2023). Decision-guided chatbots and cognitive styles in interdisciplinary learning. Computers and Education, 201, 104812. URL: https://www.sciencedirect.com/science/ article/pii/S0360131523000891. doi:https://doi.org/10.1016/j.compedu.2023.104812. Jeon, J., & Lee, S. (2023). Large language models in education: A focus on the complementary relationship between human teachers and chatgpt. Education and Information Technologies, (pp. 1–20). Kasneci, E., Sessler, K., Kuchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., G ¨ unnemann, ¨ S., Hullermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfe ¨ ffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., Stadler, M., Weller, J., Kuhn, J., & Kasneci, G. (2023). Chatgpt for good? on opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. URL: https://www.sciencedirect.com/science/article/pii/S1041608023000195. doi:https://doi.org/ 10.1016/j.lindif.2023.102274. Keng-Boon Ooi, M. A.-E. M. A. A.-S. A. C. A. C. Y. K. D. T.-L. H. A. K. K. V.-H. L. X.-M. L. A. M. P. M. E. M. N. P. R. R. N. P. R. P. S. A. S. C.-I. T. S. F. W., Garry Wei-Han Tan, & Wong, L.-W. (2023). The potential of generative artificial intelligence across disciplines: Perspectives and future directions. Journal of Computer Information Systems, 0, 1–32. URL: https://doi.org/10.1080/08874417.2023.2261010. doi:10.1080/08874417.2023.2261010. arXiv:https://doi.org/10.1080/08874417.2023.2261010. Knobloch, K., Yoon, U., & Vogt, P. M. (2011). Preferred reporting items for systematic reviews and meta-analyses (prisma) statement and publication bias. Journal of Cranio-Maxillofacial Surgery, 39, 91–92. URL: https:// www.sciencedirect.com/science/article/pii/S1010518210002180. doi:https://doi.org/10.1016/ j.jcms.2010.11.001. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521, 436–444. Lodge, J. M., Thompson, K., & Corrin, L. (2023). Mapping out a research agenda for generative artificial intelligence in tertiary education. Australasian Journal of Educational Technology, 39, 1–8. URL: https://ajet.org.au/ index.php/AJET/article/view/8695. doi:10.14742/ajet.8695. Madiega, T. (2021). Artificial intelligence act. European Parliament: European Parliamentary Research Service, . McGee, R. W. (2023). Using chatgpt to conduct literature searches: A case study. Journal of Business Ethics, 95, 165–178. Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems, 26. Mohamed, A. M. (2023). Exploring the potential of an ai-based chatbot (chatgpt) in enhancing english as a foreign language (efl) teaching: perceptions of efl faculty members. Education and Information Technologies, (pp. 1–23). Mohammed, M., Kumar, N., Zawiah, M., Al-Ashwal, F. Y., Bala, A. A., Lawal, B. K., Wada, A. S., Halboup, A., Muhammad, S., Ahmad, R. et al. (2023). Psychometric properties and assessment of knowledge, attitude, and practice towards chatgpt in pharmacy practice and education: a study protocol. Journal of Racial and Ethnic Health Disparities, (pp. 1–10). Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & Group*, P. (2009). Preferred reporting items for systematic reviews and meta-analyses: the prisma statement. Annals of internal medicine, 151, 264–269. Neelakantan, A., Xu, T., Puri, R., Radford, A., Han, J. M., Tworek, J., Yuan, Q., Tezak, N., Kim, J. W., Hallacy, C. et al. (2022). Text and code embeddings by contrastive pre-training. arXiv preprint arXiv:2201.10005, . Nickolls, J., & Dally, W. J. (2010). The gpu computing era. IEEE micro, 30, 56–69. Radford, A., Narasimhan, K., Salimans, T., Sutskever, I. et al. (2018). Improving language understanding by generative pre-training, . Reiser, R. A. (2001a). A history of instructional design and technology: Part i: A history of instructional media. Educational technology research and development, 49, 53–64. Reiser, R. A. (2001b). A history of instructional design and technology: Part ii: A history of instructional design. Educational technology research and development, 49, 57–67. Russell, S. J., & Norvig, P. (2010). Artificial intelligence a modern approach. London. Schuett, J. (2023). Risk management in the artificial intelligence act. European Journal of Risk Regulation, (pp. 1–19). Shoufan, A. (2023). Exploring students’ perceptions of chatgpt: Thematic analysis and follow-up survey. IEEE Access, 11, 38805–38818. doi:10.1109/ACCESS.2023.3268224. Smith, A., Hachen, S., Schleifer, R., Bhugra, D., Buadze, A., & Liebrenz, M. (2023). Old dog, new tricks? exploring the potential functionalities of chatgpt in supporting educational methods in social psychiatry. International Journal of Social Psychiatry, (p. 00207640231178451). Stetten, K. J. (1971). Ticcit: A delivery system designed for mass utilization., . Stojanov, A. (2023). Learning with chatgpt 3.5 as a more knowledgeable other: an autoethnographic study. International Journal of Educational Technology in Higher Education, 20, 35. Strzelecki, A. (2023). To use or not to use chatgpt in higher education? a study of students’ acceptance and use of technology. Interactive Learning Environments, 0, 1–14. URL: https://doi.org/10.1080/10494820.2023.2209881. doi:10.1080/10494820.2023.2209881. arXiv:https://doi.org/10.1080/10494820.2023.2209881 Su, J., & Yang, W. (2023). Unlocking the power of chatgpt: A framework for applying generative ai in education. ECNU Review of Education, (p. 20965311231168423). Susarla, A., Gopal, R., Thatcher, J. B., & Sarker, S. (2023). The janus effect of generative ai: Charting the path for responsible conduct of scholarly activities in information systems. Information Systems Research, . Sutskever, I., Vinyals, O., & Le, Q. V. (2014). Sequence to sequence learning with neural networks. Advances in neural information processing systems, 27. Tlili, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T., Huang, R., & Agyemang, B. (2023). What if the devil is my guardian angel: Chatgpt as a case study of using chatbots in education. Smart Learning Environments, 10, 15 Tsai, M.-L., Ong, C. W., & Chen, C.-L. (2023). Exploring the use of large language models (llms) in chemical engineering education: Building core course problem models with chat-gpt. Education for Chemical Engineers, 44, 71–95. URL: https://www.sciencedirect.com/science/article/pii/S1749772823000180. doi:https://doi.org/10.1016/j.ece.2023.05.001. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30 Wardat, Y., Tashtoush, M. A., AlAli, R., & Jarrah, A. M. (2023). Chatgpt: A revolutionary tool for teaching and learning mathematics. Eurasia Journal of Mathematics, Science and Technology Education, 19, em2286. Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E., Le, Q. V., Zhou, D. et al. (2022). Chain-of-thought prompting elicits reasoning in large language models. Advances in Neural Information Processing Systems, 35, 24824–24837. Wu, R., & Yu, Z. (2023). Do ai chatbots improve students learning outcomes? evidence from a meta-analysis. British Journal of Educational Technology, n/a. URL: https://bera-journals. onlinelibrary.wiley.com/doi/abs/10.1111/bjet.13334. doi:https://doi.org/10.1111/bjet. 13334. arXiv:https://bera-journals.onlinelibrary.wiley.com/doi/pdf/10.1111/bjet.13334. Wu, X., Zhu, X., Wu, G.-Q., & Ding, W. (2013). Data mining with big data. IEEE transactions on knowledge and data engineering, 26, 97–107. Yan, D. (2023). Impact of chatgpt on learners in a l2 writing practicum: An exploratory investigation. Education and Information Technologies, (pp. 1–25). 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