Xây dựng thang đo các yếu tố ảnh hưởng đến sự sẵn sàng ứng dụng AI tạo sinh trong dạy học
Nội dung chính của bài viết
Tóm tắt
Nghiên cứu này xây dựng và kiểm định thang đo các yếu tố ảnh hưởng đến sự sẵn sàng ứng dụng AI tạo sinh trong giáo dục của giáo viên trung học. Thang đo gồm năm yếu tố chính: Hỗ trợ từ đồng nghiệp (CS), Niềm tin theo chuẩn mực chung (NB), Quy chuẩn chủ quan (SN), Sự gắn kết với hoạt động giảng dạy (RA) và Sự sẵn sàng với AI (RE). Kết quả khảo sát với 421 giáo viên cho thấy thang đo đạt độ tin cậy cao với CS, SN và RA có mối liên hệ mạnh mẽ đến RE, trong khi NB có sự tương quan trung bình. Nghiên cứu cung cấp một công cụ đo lường hữu ích, hỗ trợ các nhà quản lý giáo dục xây dựng chính sách và chương trình đào tạo thúc đẩy chuyển đổi số. Tuy nhiên, hạn chế bao gồm phương pháp khảo sát trực tuyến và dữ liệu tự báo cáo có thể gây sai lệch. Đề xuất mở rộng phạm vi khảo sát và áp dụng thêm các phương pháp thu thập dữ liệu để tăng tính đại diện. Kết quả nghiên cứu không chỉ làm sáng tỏ các yếu tố ảnh hưởng mà còn mở ra hướng đi mới trong ứng dụng AI trong giáo dục, góp phần thúc đẩy sự đổi mới và phát triển bền vững giáo dục Việt Nam.
Từ khóa
Thang đo, hành vi, giáo viên, sự sẵn sàng, AI tạo sinh, phân tích nhân tố khẳng định
Chi tiết bài viết
Tài liệu tham khảo
Aktay, S., Gök, S., & Uzunoğlu, D. (2023). ChatGPT in Education. Türk Akademik Yayınlar Dergisi (TAY Journal), 7(2), 378-406. Retrieved from https://dergipark.org.tr/en/pub/tayjournal/issue/76658/1259832
Andrade, C. (2020). The Limitations of Online Surveys. Indian Journal of Psychological Medicine, 42(6), 575-576. https://doi.org/10.1177/025371762095749
Ayanwale, M. A., Sanusi, I. T., Adelana, O. P., Aruleba, K. D., & Oyelere, S. S. (2022). Teachers’ Readiness and Intention to Teach Artificial Intelligence in Schools. Computers and Education: Artificial Intelligence, 3, Article 100099. https://doi.org/10.1016/j.caeai.2022.100099
Browne, M. W., & Cudeck, R. (1993). Alternative Ways of Assessing Model Fit. In A. Bollen, & J. S. Long (Eds.), Testing Structural Equation Models (pp. 136-162). Beverly Hills, CA, US: SAGE Publisher.
Chai, C. S., Liang, S., & Wang, X. (2023). A Survey Study of Chinese Teachers’ Continuous Intentions to Teach Artificial Intelligence. Education and Information Technologies, 29, 14015-14034. https://doi.org/10.1007/s10639-023-12430-z
Chin, W. W., & Todd, P. A. (1995). On the Use, Usefulness, and Ease of Use of Structural Equation Modeling in MIS Research: A Note of Caution. Management Information Systems Research Center, University of Minnesota, 19(2), 237-246. https://doi.org/10.2307/249690
Chounta, I. A., Bardone, E., Raudsep, A., & Pedaste, M. (2022). Exploring Teachers’ Perceptions of Artificial Intelligence As a Tool to Support Their Practice in Estonian K-12 Education. International Journal of Artificial Intelligence in Education, 33(3), 725-755. https://doi.org/10.1007/s40593-021-00243-5
Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Hillside, NJ: Lawrence Erlbaum Associates.
Coppin, B. (2004). Artificial Intelligence Illuminated. Jones & Bartlett Learning.
Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319-340. https://doi.org/10.2307/249008
Devellis, R., & Thorpe, C. T. (2021). Scale Development Theory and Applications (5th ed.). Thousand Oaks, CA, US: SAGE Publications.
Drigas, A. S., Argyri, K., & Vrettaros, J. (2009). Decade Review (1999-2009): Artificial Intelligence Techniques in Student Modeling. Best Practices for the Knowledge Society. Knowledge, Learning, Development and Technology for All: Second World Summit on the Knowledge Society, WSKS 2009. Proceedings 2, pp. 552-564. Chania, Crete, Greece: Springer Berlin Heidelberg. Retrieved September 16-18, 2009
Fornell, C., & Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18(1), 39-50. https://doi.org/10.1177/002224378101800104
Fundi, M., Sanusi, I. T., Oyelere, S. S., & Ayere, M. (2024). Advancing AI Education: Assessing Kenyan In-Service Teachers' Preparedness for Integrating Artificial Intelligence in Competence-Based Curriculum. Computers in Human Behavior Reports, 14, Article 100412. https://doi.org/10.1016/j.chbr.2024.100412
Gerbing, D. W., & Anderson, J. C. (1988). An Updated Paradigm for Scale Development Incorporating Unidimensionality and Its Assessment. Journal of Marketing Research, 25(2), 186-192. https://doi.org/10.2307/3172650
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate Data Analysis (8th ed.). Gautam Buddha, Uttar Pradesh, India: CENGAGE Learning Publisher.
Henseler, J., Hubona, G., & Ray, P. A. (2016). Using PLS Path Modelling in New Technology Research: Updated Guidelines. Industrial Management & Data Systems, 2-20. https://doi.org/10.1108/IMDS-09-2015-0382
Hooper, D., Coughlan, J., & Mullen, M. R. (2008). Structural Equation Modeling: Guidelines for Determining Model Fit. Electronic Journal of Business Research Methods, 6(1), 53-60.
Hu, L., & Bentler, P. M. (1998). Fit Indices in Covariance Structure Modeling: Sensitivity to Underparameterized Model Misspecification. Psychological Methods, 3(4), 424-453. https://doi.org/10.1037/1082-989X.3.4.424
Jatileni, C. N., Sanusi, I. T., Olaleye, S. A., Ayanwale, M. A., Agbo, F. J., & Oyelere, P. B. (2024). Artificial Intelligence in Compulsory Level of Education: Perspectives from Namibian In-Service Teachers. Education and Information Technologies, 29(10), 12569-12596. https://doi.org/10.1007/s10639-023-12341-z
Jöhnk, J., Weißert, M., & Wyrtki, K. (2021). Ready or Not, AI Comes - An Interview Study of Organizational AI Readiness Factors. Business & Information Systems Engineering, 63(1), 5-20. https://doi.org/10.1007/s12599-020-00676-7
Kline, R. B. (2016). Principles and Practice of Structural Equation Modeling (4th ed.). The Guilford Press.
Lo, C. K. (2023). What Is the Impact of ChatGPT on Education? A Rapid Review of the Literature. Education Sciences, 13(4), Article 410. https://doi.org/10.3390/educsci13040410
Luckin, R., Cukurova, M., Kent, C., & Boulay, B. D. (2022). Empowering Educators to be AI-Ready. Computers and Education: Artificial Intelligence, 3, Article 100076. https://doi.org/10.1016/j.caeai.2022.100076
Mai, D. T., Da, C. V., & Hanh, N. V. (2024). The Use of ChatGPT in Teaching and Learning: a Systematic Review Through SWOT Analysis Approach. Frontiers in Education, 9, Article 1328769. https://doi.org/10.3389/feduc.2024.1328769
Miao, F., & Holmes, W. (2023). Guidance for Generative AI in Education and Research. Paris, France: UNESCO. https://doi.org/10.54675/EWZM9535
Miller, G. (1956). The Magical Number Seven, Plus or Minus Two: Some Limits on Our Capacity for Processing Information. The Psychological Review, 63, 81-97.
Montano, D. E., & Kasprzyk, D. (2008). Theory of Reasoned Action, Theory of Planned Behavior, and the Integrated Behavioral Model. In B. K. Karen Glanz, Health Behavior: Theory, Research and Practice (pp. 67-92). CA: Jossey-Bass.
Mulaik, S. A., James, L. R., Van Alstine, J., Bennett, N., Lind, S., & Stilwell, C. D. (1989). Evaluation of Goodness-of-fit Indices for Structural Equation Models. Psychological Bulletin, 105(3), 430-445. https://doi.org/10.1037/0033-2909.105.3.430
Nguyen, H. D., Nguyen, H. N., & Ta, T. T. (2024). Factors Affecting the Implementation of STEAM Education Among Primary School Teachers in Various Countries and Vietnamese Educators: Comparative Analysis. Education 3-13, 1-15. https://doi.org/10.1080/03004279.2024.2318239
Nguyen, H. N., & Ta, T. T. (2024). Developing Scale to Assess Teachers' Behaviour for Implementing The 2018 General Education Program: A Study Based on The Theory of Planned Behavior. HNUE Journal of Science, 69(2), 3-14. https://doi.org/10.18173/2354-1075.2024-0018
Nunnally, J. C., & Bernstein, I. H. (1994). The Assessment of Reliability. In Psychometric Theory (Vol. 3, pp. 248-292). New York, NY, US: McGraw-Hill.
Osetskyi, V., Vitrenko, A., Tatomyr, I., Bilan, S., & Hirnyk, Y. (2020). Artificial Intelligence Application in Education: Financial Implications and Prospects. Financial and Credit Activity Problems of Theory and Practice, 2(33), 574-584. https://doi.org/10.18371/fcaptp.v2i33.207246
Pardos, Z. A., & Bhandari, S. (2024). ChatGPT-Generated Help Produces Learning Gains Equivalent to Human Tutor-Authored Help on Mathematics Skills. Plos one 19.5, 19(5), Article e0304013. https://doi.org/10.1371/journal.pone.0304013
Pedró, F., Subosa, M., Rivas, A., & Valverde, P. (2019). Artificial Intelligence in Education: Challenges and Opportunities for Sustainable Development UNESCO (Vol. Working Papers on Education Policy). Paris, France: UNESCO. Retrieved from https://unesdoc.unesco.org/ark:/48223/pf0000366994
Peres, R., Schreier, M., Schweidel, D., & Sorescu, A. (2023). On ChatGPT and Beyond: How Generative Artificial Intelligence May Affect Research, Teaching, and Practice. International Journal of Research in Marketing, 40(2), 269-275. https://doi.org/10.1016/j.ijresmar.2023.03.001
Russell, S. J., & Norvig, P. (2016). Artificial Intelligence: a Modern Approach. Pearson.
Seryapina, Y. S. (2018). The Concept of “Readiness for Pedagogical Activity”: Motivational Readiness, Psychological Readiness, Readiness to Innovative Activity. Вестник Южно-Уральского государственного университета. Серия: Образование. Педагогические науки, 10(4), 77-86.
Ta, T. T., & Nguyen, T. N. (2022). A Comparison of Using CB-SEM and PLS-SEM to Assess Training Effectiveness Evaluation Model for Teacher’s Online Continuing Professional Development. Ho Chi Minh City University of Education Journal of Science, 19(2), 213-228. https://doi.org/10.54607/hcmue.js.19.2.3306(2022)
Tang, T., Sha, J., Zhao, Y., Wang, S., Wang, Z., & Shen, S. (2024). Unveiling the Efficacy of ChatGPT in Evaluating Critical Thinking Skills Through Peer Feedback Analysis: Leveraging Existing Classification Criteria. Thinking Skills and Creativity, 53, Article 101607. https://doi.org/10.1016/j.tsc.2024.101607
UNESCO. (2022). K-12 AI Curricula: a Mapping of Government-endorsed AI Curricula. Paris, France. Retrieved from https://unesdoc.unesco.org/ark:/48223/pf0000380602
Venkatesh, V., & Davis, F. D. (2000). A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Management Science, 46(2), 186-204. https://doi.org/10.1287/mnsc.46.2.186.11926
Wang, X. (2023). Preparing for AI-Enhanced Education: Conceptualizing and Empirically Examining Teachers’ AI Readiness. Computers in Human Behavior, 146, Article 107798. https://doi.org/10.1016/j.chb.2023.107798
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(7), Article em2286. https://doi.org/10.29333/ejmste/13272
Wu, T.-T., Lee, H.-Y., & Huang, Y.-M. (2024). Promoting Self-Regulation Progress and Knowledge Construction in Blended Learning via ChatGPT-Based Learning Aid. Journal of Educational Computing Research, 61(8), 3-31. https://doi.org/10.1177/07356331231191125
Wyk, M. M. (2024). Is ChatGPT an Opportunity or a Threat? Preventive Strategies Employed by Academics Related to a GenAI-based LLM at a Faculty of Education. Journal of Applied Learning and Teaching, 7(1), 35-45. https://doi.org/10.37074/jalt.2024.7.1.15
Zong, M., & Krishnamachari, B. (2023). Solving Math Word Problems Concerning Systems of Equations with GPT Models. Machine Learning with Applications, 14, Article 100506. https://doi.org/10.1016/j.mlwa.2023.100506
Andrade, C. (2020). The Limitations of Online Surveys. Indian Journal of Psychological Medicine, 42(6), 575-576. https://doi.org/10.1177/025371762095749
Ayanwale, M. A., Sanusi, I. T., Adelana, O. P., Aruleba, K. D., & Oyelere, S. S. (2022). Teachers’ Readiness and Intention to Teach Artificial Intelligence in Schools. Computers and Education: Artificial Intelligence, 3, Article 100099. https://doi.org/10.1016/j.caeai.2022.100099
Browne, M. W., & Cudeck, R. (1993). Alternative Ways of Assessing Model Fit. In A. Bollen, & J. S. Long (Eds.), Testing Structural Equation Models (pp. 136-162). Beverly Hills, CA, US: SAGE Publisher.
Chai, C. S., Liang, S., & Wang, X. (2023). A Survey Study of Chinese Teachers’ Continuous Intentions to Teach Artificial Intelligence. Education and Information Technologies, 29, 14015-14034. https://doi.org/10.1007/s10639-023-12430-z
Chin, W. W., & Todd, P. A. (1995). On the Use, Usefulness, and Ease of Use of Structural Equation Modeling in MIS Research: A Note of Caution. Management Information Systems Research Center, University of Minnesota, 19(2), 237-246. https://doi.org/10.2307/249690
Chounta, I. A., Bardone, E., Raudsep, A., & Pedaste, M. (2022). Exploring Teachers’ Perceptions of Artificial Intelligence As a Tool to Support Their Practice in Estonian K-12 Education. International Journal of Artificial Intelligence in Education, 33(3), 725-755. https://doi.org/10.1007/s40593-021-00243-5
Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Hillside, NJ: Lawrence Erlbaum Associates.
Coppin, B. (2004). Artificial Intelligence Illuminated. Jones & Bartlett Learning.
Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319-340. https://doi.org/10.2307/249008
Devellis, R., & Thorpe, C. T. (2021). Scale Development Theory and Applications (5th ed.). Thousand Oaks, CA, US: SAGE Publications.
Drigas, A. S., Argyri, K., & Vrettaros, J. (2009). Decade Review (1999-2009): Artificial Intelligence Techniques in Student Modeling. Best Practices for the Knowledge Society. Knowledge, Learning, Development and Technology for All: Second World Summit on the Knowledge Society, WSKS 2009. Proceedings 2, pp. 552-564. Chania, Crete, Greece: Springer Berlin Heidelberg. Retrieved September 16-18, 2009
Fornell, C., & Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18(1), 39-50. https://doi.org/10.1177/002224378101800104
Fundi, M., Sanusi, I. T., Oyelere, S. S., & Ayere, M. (2024). Advancing AI Education: Assessing Kenyan In-Service Teachers' Preparedness for Integrating Artificial Intelligence in Competence-Based Curriculum. Computers in Human Behavior Reports, 14, Article 100412. https://doi.org/10.1016/j.chbr.2024.100412
Gerbing, D. W., & Anderson, J. C. (1988). An Updated Paradigm for Scale Development Incorporating Unidimensionality and Its Assessment. Journal of Marketing Research, 25(2), 186-192. https://doi.org/10.2307/3172650
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate Data Analysis (8th ed.). Gautam Buddha, Uttar Pradesh, India: CENGAGE Learning Publisher.
Henseler, J., Hubona, G., & Ray, P. A. (2016). Using PLS Path Modelling in New Technology Research: Updated Guidelines. Industrial Management & Data Systems, 2-20. https://doi.org/10.1108/IMDS-09-2015-0382
Hooper, D., Coughlan, J., & Mullen, M. R. (2008). Structural Equation Modeling: Guidelines for Determining Model Fit. Electronic Journal of Business Research Methods, 6(1), 53-60.
Hu, L., & Bentler, P. M. (1998). Fit Indices in Covariance Structure Modeling: Sensitivity to Underparameterized Model Misspecification. Psychological Methods, 3(4), 424-453. https://doi.org/10.1037/1082-989X.3.4.424
Jatileni, C. N., Sanusi, I. T., Olaleye, S. A., Ayanwale, M. A., Agbo, F. J., & Oyelere, P. B. (2024). Artificial Intelligence in Compulsory Level of Education: Perspectives from Namibian In-Service Teachers. Education and Information Technologies, 29(10), 12569-12596. https://doi.org/10.1007/s10639-023-12341-z
Jöhnk, J., Weißert, M., & Wyrtki, K. (2021). Ready or Not, AI Comes - An Interview Study of Organizational AI Readiness Factors. Business & Information Systems Engineering, 63(1), 5-20. https://doi.org/10.1007/s12599-020-00676-7
Kline, R. B. (2016). Principles and Practice of Structural Equation Modeling (4th ed.). The Guilford Press.
Lo, C. K. (2023). What Is the Impact of ChatGPT on Education? A Rapid Review of the Literature. Education Sciences, 13(4), Article 410. https://doi.org/10.3390/educsci13040410
Luckin, R., Cukurova, M., Kent, C., & Boulay, B. D. (2022). Empowering Educators to be AI-Ready. Computers and Education: Artificial Intelligence, 3, Article 100076. https://doi.org/10.1016/j.caeai.2022.100076
Mai, D. T., Da, C. V., & Hanh, N. V. (2024). The Use of ChatGPT in Teaching and Learning: a Systematic Review Through SWOT Analysis Approach. Frontiers in Education, 9, Article 1328769. https://doi.org/10.3389/feduc.2024.1328769
Miao, F., & Holmes, W. (2023). Guidance for Generative AI in Education and Research. Paris, France: UNESCO. https://doi.org/10.54675/EWZM9535
Miller, G. (1956). The Magical Number Seven, Plus or Minus Two: Some Limits on Our Capacity for Processing Information. The Psychological Review, 63, 81-97.
Montano, D. E., & Kasprzyk, D. (2008). Theory of Reasoned Action, Theory of Planned Behavior, and the Integrated Behavioral Model. In B. K. Karen Glanz, Health Behavior: Theory, Research and Practice (pp. 67-92). CA: Jossey-Bass.
Mulaik, S. A., James, L. R., Van Alstine, J., Bennett, N., Lind, S., & Stilwell, C. D. (1989). Evaluation of Goodness-of-fit Indices for Structural Equation Models. Psychological Bulletin, 105(3), 430-445. https://doi.org/10.1037/0033-2909.105.3.430
Nguyen, H. D., Nguyen, H. N., & Ta, T. T. (2024). Factors Affecting the Implementation of STEAM Education Among Primary School Teachers in Various Countries and Vietnamese Educators: Comparative Analysis. Education 3-13, 1-15. https://doi.org/10.1080/03004279.2024.2318239
Nguyen, H. N., & Ta, T. T. (2024). Developing Scale to Assess Teachers' Behaviour for Implementing The 2018 General Education Program: A Study Based on The Theory of Planned Behavior. HNUE Journal of Science, 69(2), 3-14. https://doi.org/10.18173/2354-1075.2024-0018
Nunnally, J. C., & Bernstein, I. H. (1994). The Assessment of Reliability. In Psychometric Theory (Vol. 3, pp. 248-292). New York, NY, US: McGraw-Hill.
Osetskyi, V., Vitrenko, A., Tatomyr, I., Bilan, S., & Hirnyk, Y. (2020). Artificial Intelligence Application in Education: Financial Implications and Prospects. Financial and Credit Activity Problems of Theory and Practice, 2(33), 574-584. https://doi.org/10.18371/fcaptp.v2i33.207246
Pardos, Z. A., & Bhandari, S. (2024). ChatGPT-Generated Help Produces Learning Gains Equivalent to Human Tutor-Authored Help on Mathematics Skills. Plos one 19.5, 19(5), Article e0304013. https://doi.org/10.1371/journal.pone.0304013
Pedró, F., Subosa, M., Rivas, A., & Valverde, P. (2019). Artificial Intelligence in Education: Challenges and Opportunities for Sustainable Development UNESCO (Vol. Working Papers on Education Policy). Paris, France: UNESCO. Retrieved from https://unesdoc.unesco.org/ark:/48223/pf0000366994
Peres, R., Schreier, M., Schweidel, D., & Sorescu, A. (2023). On ChatGPT and Beyond: How Generative Artificial Intelligence May Affect Research, Teaching, and Practice. International Journal of Research in Marketing, 40(2), 269-275. https://doi.org/10.1016/j.ijresmar.2023.03.001
Russell, S. J., & Norvig, P. (2016). Artificial Intelligence: a Modern Approach. Pearson.
Seryapina, Y. S. (2018). The Concept of “Readiness for Pedagogical Activity”: Motivational Readiness, Psychological Readiness, Readiness to Innovative Activity. Вестник Южно-Уральского государственного университета. Серия: Образование. Педагогические науки, 10(4), 77-86.
Ta, T. T., & Nguyen, T. N. (2022). A Comparison of Using CB-SEM and PLS-SEM to Assess Training Effectiveness Evaluation Model for Teacher’s Online Continuing Professional Development. Ho Chi Minh City University of Education Journal of Science, 19(2), 213-228. https://doi.org/10.54607/hcmue.js.19.2.3306(2022)
Tang, T., Sha, J., Zhao, Y., Wang, S., Wang, Z., & Shen, S. (2024). Unveiling the Efficacy of ChatGPT in Evaluating Critical Thinking Skills Through Peer Feedback Analysis: Leveraging Existing Classification Criteria. Thinking Skills and Creativity, 53, Article 101607. https://doi.org/10.1016/j.tsc.2024.101607
UNESCO. (2022). K-12 AI Curricula: a Mapping of Government-endorsed AI Curricula. Paris, France. Retrieved from https://unesdoc.unesco.org/ark:/48223/pf0000380602
Venkatesh, V., & Davis, F. D. (2000). A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Management Science, 46(2), 186-204. https://doi.org/10.1287/mnsc.46.2.186.11926
Wang, X. (2023). Preparing for AI-Enhanced Education: Conceptualizing and Empirically Examining Teachers’ AI Readiness. Computers in Human Behavior, 146, Article 107798. https://doi.org/10.1016/j.chb.2023.107798
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(7), Article em2286. https://doi.org/10.29333/ejmste/13272
Wu, T.-T., Lee, H.-Y., & Huang, Y.-M. (2024). Promoting Self-Regulation Progress and Knowledge Construction in Blended Learning via ChatGPT-Based Learning Aid. Journal of Educational Computing Research, 61(8), 3-31. https://doi.org/10.1177/07356331231191125
Wyk, M. M. (2024). Is ChatGPT an Opportunity or a Threat? Preventive Strategies Employed by Academics Related to a GenAI-based LLM at a Faculty of Education. Journal of Applied Learning and Teaching, 7(1), 35-45. https://doi.org/10.37074/jalt.2024.7.1.15
Zong, M., & Krishnamachari, B. (2023). Solving Math Word Problems Concerning Systems of Equations with GPT Models. Machine Learning with Applications, 14, Article 100506. https://doi.org/10.1016/j.mlwa.2023.100506