Pros and Cons of Generative Artificial Intelligence in Teaching-Learning Process: A Sequential Explanatory Design
Carlito Sagocsoc Jr | Bazil T. Sabacajan | Lailane Lopena | Clyde Ryan Along
Discipline: Artificial Intelligence
Abstract:
Generative Artificial Intelligence (GAI) is a type of AI that can create new content, e.g., text, based on patterns and structures learned from existing data, and its application is pervasive in several areas, including education. This study, therefore, sought to assess the Junior High School (JHS) teachers' perception of the pros and cons of GAI in teaching and learning. It also aimed to determine the challenges teachers faced with GAI. This study utilized the sequential explanatory design, utilizing 50 teachers in a Junior High- School Integrated School in Sagay District, Schools Division of Camiguin, for the school year 2023-2024. Results revealed that the teacher-respondents perceived Generative AI to have a "Moderate Extent" of advantages as they believed in its potential application in essential improvements in teaching. However, they further reported a "High Extent" of disadvantages, notably its significant influence on conventional teaching-learning practices. The qualitative results also demonstrated several challenges, including factors limited teacher oversight of student AI Use, limited access to information, decreased learner retention, the overshadowing of traditional teaching methods, and the encouragement of academic dishonesty. In light of the results, the article suggests that policies be instituted around the responsible and effective use of generative AI in schools.
References:
- Adams, J., Roberts, A., Obermaier, L., Ravuri, B., & Moja, O. (2023, July). Uses of artificial intelligence in higher education. In EdMedia+ Innovate Learning (pp. 1181–1186). Association for the Advancement of Computing in Education (AACE).
- Akgun, S., & Greenhow, C. (2021). Artificial intelligence in education: Addressing ethical challenges in K–12 settings. AI and Ethics, 1, 1–10. https://doi.org/10.1007/s43681-021-00038-5
- AlAfnan, M. A., Dishari, S., Jovic, M., & Lomidze, K. (2023). ChatGPT as an educational tool: Opportunities, challenges, and recommendations for communication, business writing, and composition courses. Journal of Artificial Intelligence and Technology, 3(2), 60–68. https://doi.org/10.54364/jait.2023.v3i2.111
- Amaro, I., Della Greca, A., Francese, R., Tortora, G., & Tucci, C. (2023, July). AI unreliable answers: A case study on ChatGPT. In International Conference on Human-Computer Interaction (pp. 23–40). Springer Nature Switzerland.
- Ayala-Pazmiño, M. (2023). Artificial intelligence in education: Exploring the potential benefits and risks. Digital Publisher CEIT, 8(3), 892–899.
- Babitha, M. M., Sushma, C., & Gudivada, V. K. (2022). Trends of artificial intelligence for online exams in education. International Journal of Early Childhood Special Education, 14(1), 2457–2463.
- Bahrini, A., Khamoshifar, M., Abbasimehr, H., Riggs, R. J., Esmaeili, M., Majdabadkohne, R. M., & Pasehvar, M. (2023, April). ChatGPT: Applications, opportunities, and threats. In 2023 Systems and Information Engineering Design Symposium (SIEDS) (pp. 274–279). IEEE.
- Bozkurt, A., Xiao, J., Lambert, S., Pazurek, A., Crompton, H., Koseoglu, S., ... & Jandrić, P. (2023). Speculative futures on ChatGPT and generative artificial intelligence (AI): A collective reflection from the educational landscape. Asian Journal of Distance Education, 18(1).
- Bozkurt, A. (2023). Generative artificial intelligence (AI) powered conversational educational agents: The inevitable paradigm shift. Asian Journal of Distance Education, 18(1).
- Chan, C. K. Y., & Tsi, L. H. (2023). The AI revolution in education: Will AI replace or assist teachers in higher education? arXiv. https://arxiv.org/abs/2305.01185
- Chassignol, M., Khoroshavin, A., Klimova, A., & Bilyatdinova, A. (2018). Artificial intelligence trends in education: A narrative overview. Procedia Computer Science, 136, 16–24. https://doi.org/10.1016/j.procs.2018.08.233
- Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264–75278. https://doi.org/10.1109/ACCESS.2020.2988510
- Conijn, R., Kahr, P., & Snijders, C. (2023). The effects of explanations in automated essay scoring systems on student trust and motivation. Journal of Learning Analytics, 10(1), 37–53. https://doi.org/10.21125/learninganalytics.2023.v10i1.1234
- Devedžić, V. (2004). Web intelligence and artificial intelligence in education. Journal of Educational Technology & Society, 7(4), 29–39.
- Duboust, O. (2023, November 24). 'Unreliable research assistant’: False outputs from AI chatbots pose risk to science, report says. Euronews. https://www.euronews.com/next/2023/11/20/unreliable-research-assistant-false-outputs-from-ai-chatbots-pose-risk-to-science-report-s
- Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., ... & 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. https://doi.org/10.1016/j.ijinfomgt.2023.102642
- Fang, Y., Chen, P., Cai, G., Lau, F. C., Liew, S. C., & Han, G. (2019). Outage-limit-approaching channel coding for future wireless communications: Root-protograph low-density parity-check codes. IEEE Vehicular Technology Magazine, 14(2), 85–93. https://doi.org/10.1109/MVT.2019.2905537
- Holmes, W., Porayska-Pomsta, K., Holstein, K., Sutherland, E., Baker, T., Shum, S. B., ... & Koedinger, K. R. (2021). Ethics of AI in education: Towards a community-wide framework. International Journal of Artificial Intelligence in Education, 31(1), 1–23. https://doi.org/10.1007/s40593-020-00239-1
- Huang, A. Y., Lu, O. H., & Yang, S. J. (2023). Effects of artificial intelligence–enabled personalized recommendations on learners’ engagement, motivation, and outcomes in a flipped classroom. Computers & Education, 194, 104684. https://doi.org/10.1016/j.compedu.2022.104684
- Hwang, G. J., & Chien, S. Y. (2022). Definition, roles, and potential research issues of the metaverse in education: An artificial intelligence perspective. Computers and Education: Artificial Intelligence, 3, 100082. https://doi.org/10.1016/j.caeai.2022.100082
- Hwang, G. J., Xie, H., Wah, B. W., & Gašević, D. (2020). Vision, challenges, roles and research issues of artificial intelligence in education. Computers and Education: Artificial Intelligence, 1, 100001. https://doi.org/10.1016/j.caeai.2020.100001
- Jabar, M., Chiong-Javier, E., & Pradubmook Sherer, P. (2024). Qualitative ethical technology assessment of artificial intelligence (AI) and the internet of things (IoT) among Filipino Gen Z members: Implications for ethics education in higher learning institutions. Asia Pacific Journal of Education, 1–15. https://doi.org/10.1080/02188791.2024.2303477
- Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., & Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274
- Khalil, M., & Er, E. (2023, June). Will ChatGPT get you caught? Rethinking of plagiarism detection. In International Conference on Human-Computer Interaction (pp. 475–487). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-35650-7_33
- Kleebayoon, A., & Wiwanitkit, V. (2023). Artificial intelligence, chatbots, plagiarism and basic honesty: Comment. Cellular and Molecular Bioengineering, 16(2), 173–174. https://doi.org/10.1007/s12195-023-00755-6
- Lee, H., & Hwang, Y. (2022). Technology-enhanced education through VR-making and metaverse-linking to foster teacher readiness and sustainable learning. Sustainability, 14(8), 4786. https://doi.org/10.3390/su14084786
- Maghsudi, S., Lan, A., Xu, J., & van der Schaar, M. (2021). Personalized education in the artificial intelligence era: What to expect next. IEEE Signal Processing Magazine, 38(3), 37–50. https://doi.org/10.1109/MSP.2021.3061992
- Minn, S. (2022). AI-assisted knowledge assessment techniques for adaptive learning environments. Computers and Education: Artificial Intelligence, 3, 100050. https://doi.org/10.1016/j.caeai.2022.100050
- Mondal, S., Das, S., & Vrana, V. G. (2023). How to bell the cat? A theoretical review of generative artificial intelligence towards digital disruption in all walks of life. Technologies, 11(2), 44. https://doi.org/10.3390/technologies11020044
- Pedro, F., Subosa, M., Rivas, A., & Valverde, P. (2019). Artificial intelligence in education: Challenges and opportunities for sustainable development. UNESCO. https://unesdoc.unesco.org/ark:/48223/pf0000366994
- Potgieter, I. (2020). Privacy concerns in educational data mining and learning analytics. The International Review of Information Ethics, 28, 22–28. https://www.i-r-i-e.net/inhalt/028/IRIE-028-Potgieter.pdf
- Schlippe, T., Stierstorfer, Q., Koppel, M. T., & Libbrecht, P. (2022, July). Explainability in automatic short answer grading. In International Conference on Artificial Intelligence in Education Technology (pp. 69–87). Springer Nature Singapore. https://doi.org/10.1007/978-981-19-0742-6_6
- Schlippe, T., & Sawatzki, J. (2021, July). Cross-lingual automatic short answer grading. In International Conference on Artificial Intelligence in Education Technology (pp. 117–129). Springer Nature Singapore. https://doi.org/10.1007/978-981-16-4983-0_11
- Shah, P. (2023). AI and the future of education: Teaching in the age of artificial intelligence. John Wiley & Sons.
- Sharma, H. (2022). How short or long should be a questionnaire for any research? Researchers’ dilemma in deciding the appropriate questionnaire length. Saudi Journal of Anaesthesia, 16(1), 65–68. https://doi.org/10.4103/sja.sja_1241_21
- Taeihagh, A. (2021). Governance of artificial intelligence. Policy and Society, 40(2), 137–157. https://doi.org/10.1080/14494035.2021.1928371
- Thurzo, A., Strunga, M., Urban, R., Surovková, J., & Afrashtehfar, K. I. (2023). Impact of artificial intelligence on dental education: A review and guide for curriculum update. Education Sciences, 13(2), 150. https://doi.org/10.3390/educsci13020150
- Vincent-Lancrin, S., & Van der Vlies, R. (2020). Trustworthy artificial intelligence (AI) in education: Promises and challenges. OECD. https://doi.org/10.1787/a6c90fa3-en
- Yang, S. J., Ogata, H., Matsui, T., & Chen, N. S. (2021). Human-centered artificial intelligence in education: Seeing the invisible through the visible. Computers and Education: Artificial Intelligence, 2, 100008. https://doi.org/10.1016/j.caeai.2021.100008
- Yilmaz, R., & Yilmaz, F. G. K. (2023). Augmented intelligence in programming learning: Examining student views on the use of ChatGPT for programming learning. Computers in Human Behavior: Artificial Humans, 1(2), 100005. https://doi.org/10.1016/j.chbah.2023.100005
- Zhai, X., Chu, X., Chai, C. S., Jong, M. S. Y., Istenic, A., Spector, M., ... & Li, Y. (2021). A review of artificial intelligence (AI) in education from 2010 to 2020. Complexity, 2021, Article ID 8812542. https://doi.org/10.1155/2021/8812542.
- Zhang, C., Zhang, C., Li, C., Qiao, Y., Zheng, S., Dam, S. K., ... & Hong, C. S. (2023). One small step for generative AI, one giant leap for AGI: A complete survey on ChatGPT in AIGC era. arXiv preprint arXiv:2304.06488. https://arxiv.org/abs/2304.06488