ATTITUDES TOWARDS LARGE LANGUAGE MODELS AND MOTIVATIONS FOR THEIR USE: BASIS FOR CLASSROOM INTEGRATION

Jeffrey A. Rajik

Abstract


This research investigates faculty and students’ attitudes toward Large Language Models (LLMs) and their motivations for their use in classrooms. Utilizing descriptive quantitative methods, including the modified General Attitudes towards Artificial Intelligence Scale (GAAIS) and the Questionnaire of AI Use Motives (QAIUM), data were collected from 24 faculty members and 120 students across eight undergraduate programs of the College of Arts and Sciences of Mindanao State University – Tawi-Tawi College of Technology and Oceanography in the Philippines.  Results reveal significant differences between faculty and students in their attitudes and motivations toward LLMs.  Faculty express greater concerns regarding ethical implications, transparency, and reliability, along with apprehension regarding the potential loss of personal connections and the value of human interaction in the learning process. On the other hand, students display greater enthusiasm and a willingness to engage with LLMs.  As education is shifting to a digital future, these findings highlight the need for targeted professional development for faculty to effectively integrate LLMs into teaching practices and to address the ethical considerations and equity issues associated with their use. Furthermore, this study emphasizes the importance of considering diverse perspectives when implementing LLMs in educational settings, as it can lead to more effective and balanced integration strategies.

 

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artificial intelligence, large language models, LLMs in classrooms, attitudes toward LLMs, LLMs use motivations

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Alier, M., Casañ, M. J., & Filvà, D. A. (2023, October). Smart Learning Applications: Leveraging LLMs for Contextualized and Ethical Educational Technology. In International conference on technological ecosystems for enhancing multiculturality (pp. 190-199). Singapore: Springer Nature Singapore. Retrieved from https://link.springer.com/chapter/10.1007/978-981-97-1814-6_18

Bakun, C. J. D., Kamlian, D. T., & Rajik, J. A. (2023). Adapting to the Dominant Language: Challenges and Coping Strategies. Int J Multidiscip Res [Internet].[Cited 2024 Jan 15], 5(6). https://doi.org/10.36948/ijfmr.2023.v05i06.11164

Chan, C. K. Y., & Tsi, L. H. (2023). The AI revolution in education: Will AI replace or assist teachers in higher education?. arXiv preprint arXiv:2305.01185. https://doi.org/10.48550/arXiv.2305.01185

Chan, C. K. Y., & Hu, W. (2023). Students’ voices on generative AI: Perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education, 20(1), 43. https://doi.org/10.1186/s41239-023-00411-8

Christensen, R., & Knezek, G. (2017). Readiness for integrating mobile learning in the classroom: Challenges, preferences and possibilities. Computers in Human Behavior, 76, 112-121. https://doi.org/10.1016/j.chb.2017.07.014

Farah, J. C. (2023). A Conceptual Framework for Integrating Conversational Agents in Digital Education (No. 10757). EPFL. https://doi.org/10.5075/epfl-thesis-10757

Graefen, B., & Fazal, N. (2024). From Chat bots to Virtual Tutors: An Overview of Chat GPT's Role in the Future of Education. Archives of Pharmacy Practice, 15(2-2024), 43-52. https://doi.org/10.51847/TOuppjEDSX

Hadi, M. U., Al Tashi, Q., Shah, A., Qureshi, R., Muneer, A., Irfan, M., ... & Shah, M. (2024). Large language models: a comprehensive survey of its applications, challenges, limitations, and future prospects. Authorea Preprints. https://doi.org/10.36227/techrxiv.23589741.v6

Hagos, D. H., Battle, R., & Rawat, D. B. (2024). Recent advances in generative ai and large language models: Current status, challenges, and perspectives. IEEE Transactions on Artificial Intelligence. https://doi.org/10.1109/TAI.2024.3444742

Hutson, J. (2024). Rethinking Plagiarism in the Era of Generative AI. Journal of Intelligent Communication, 4(1), 20-31. https://doi.org/10.54963/jic.v4i1.220

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, 28(12), 15873-15892. https://doi.org/10.1007/s10639-023-11834-1

Johnsen, M. (2024). Large Language Models (LLMs). Maria Johnsen.

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

Lin, Z., Guan, S., Zhang, W., Zhang, H., Li, Y., & Zhang, H. (2024). Towards trustworthy LLMs: a review on debiasing and dehallucinating in large language models. Artificial Intelligence Review, 57(9), 1-50. https://doi.org/10.1007/s10462-024-10896-y

Liu, D., Huang, R., Chen, Y., Adarkwah, M. A., Zhang, X., Li, X., ... & Da, T. (2024). Personalized Tutoring Through Conversational Agents. In Using Educational Robots to Enhance Learning: An Analysis of 100 Academic Articles (pp. 59-85). Singapore: Springer Nature Singapore. https://doi.org/10.1007/978-981-97-5826-5_4

Pandita, A., & Kiran, R. (2023). The technology interface and student engagement are significant stimuli in sustainable student satisfaction. Sustainability, 15(10), 7923. https://doi.org/10.3390/su15107923

Raiaan, M. A. K., Mukta, M. S. H., Fatema, K., Fahad, N. M., Sakib, S., Mim, M. M. J., ... & Azam, S. (2024). A review on large Language Models: Architectures, applications, taxonomies, open issues and challenges. IEEE Access. https://doi.org/10.1109/ACCESS.2024.3365742

Rajik, J. (2023). Students’ Perceptions of the Influence of Teacher’s Questions on Their Reading Comprehension. Journal of English Language Teaching and Applied Linguistics, 5(4), 101-112. https://doi.org/10.32996/jeltal.2023.5.4.11

Rajik, J., & Tarusan, M. A. (2023). A Grammar Sketch of Southern Sinama Language. International Journal of Linguistics Studies, 3(1), 09-61.

https://doi.org/10.32996/ijls.2023.3.1.2

Rane, N., Choudhary, S. P., & Rane, J. (2024). Acceptance of artificial intelligence: key factors, challenges, and implementation strategies. Journal of Applied Artificial Intelligence, 5(2), 50-70. https://doi.org/10.48185/jaai.v5i2.1017

Schepman, A., & Rodway, P. (2020). Initial validation of the general attitudes towards Artificial Intelligence Scale. Computers in human behavior reports, 1, 100014. https://doi.org/10.1016/j.chbr.2020.100014

Schindler, L. A., Burkholder, G. J., Morad, O. A., & Marsh, C. (2017). Computer-based technology and student engagement: a critical review of the literature. International journal of educational technology in higher education, 14, 1-28. https://doi.org/10.1186/s41239-017-0063-0

Selwyn, N. (2021). Education and technology: Key issues and debates. Bloomsbury Publishing.

Shernoff, D. J., Sinha, S., Bressler, D. M., & Ginsburg, L. (2017). Assessing teacher education and professional development needs for the implementation of integrated approaches to STEM education. International journal of STEM education, 4, 1-16. https://doi.org/10.1186/s40594-017-0068-1

Slavin, R. E. (2020). How evidence-based reform will transform research and practice in education. Educational Psychologist, 55(1), 21-31. https://doi.org/10.1080/00461520.2019.1611432

Smith, R., Cubino, M., & McKeon, E. (2024). The AI Revolution in Customer Service and Support: A Practical Guide to Impactful Deployment of AI to Best Serve Your Customers. Pearson Education. Retrieved from https://www.pearson.com/en-us/subject-catalog/p/the-ai-revolution-in-customer-service-and-support-a-practical-guide-to-impactful-deployment-of-ai-models/P200000012137/9780138286651

Subramanian, S. (2024). Large Language Model-Based Solutions: How to Deliver Value with Cost-Effective Generative AI Applications. John Wiley & Sons. Retrieved from https://www.wiley.com/en-au/Large+Language+Model-Based+Solutions%3A+How+to+Deliver+Value+with+Cost-Effective+Generative+AI+Applications-p-9781394240722

Upadhyay, A., Farahmand, E., Muñoz, I., Akber Khan, M., & Witte, N. (2024). Influence of LLMs on Learning and Teaching in Higher Education. http://dx.doi.org/10.2139/ssrn.4716855

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Proceedings of the 31st international conference on neural information processing systems.

Wibowo, G. A., Rahman, A., & Anis, M. (2023). The impact of ChatGPT use on the quality of academic support for students. Technology and Society Perspectives (TACIT), 1(3), 132-138. https://doi.org/10.61100/tacit.v1i3.69

Yu, H. (2024). The application and challenges of ChatGPT in educational transformation: New demands for teachers' roles. Heliyon. https://doi.org/10.1016/j.heliyon.2024.e24289

Yurt, E. & Kasarci, I. (2024). A Questionnaire of Artificial Intelligence Use Motives: A contribution to investigating the connection between AI and motivation. International Journal of Technology in Education (IJTE), 7(2), 308-325. https://doi.org/10.46328/ijte.725

Zhao, Y., & Cziko, G. A. (2001). Teacher adoption of technology: A perceptual control theory perspective. Journal of technology and teacher education, 9(1), 5-30. Retrieved from https://www.researchgate.net/publication/255566917_Teacher_Adoption_of_Technology_A_Perceptual_Control_Theory_Perspective

Zhi, R., & Wang, Y. (2024). On the relationship between EFL students' attitudes toward artificial intelligence, teachers' immediacy and teacher-student rapport, and their willingness to communicate. System, 124, 103341. https://doi.org/10.1016/j.system.2024.103341




DOI: http://dx.doi.org/10.46827/ejes.v11i12.5727

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