The Future of Education
How Students’ Perceptions and Attitudes Towards AI Inform Teacher Preparation and Professional Development
Betsy Kells, University of Pennsylvania (United States)
Christina Frei, University of Pennsylvania (United States)
Abstract
keywords:generative artificial intelligence (AI); transparency; technology acceptance model; co-construction of AI policies
This presentation reports findings from a longitudinal study conducted by a Language Center at a large research university in the United States. The study investigates undergraduate students’ and instructors’ perceptions and uses of generative artificial intelligence for world language learning. Guided by an expanded Technology Acceptance Model (Chen et al., 2024) that incorporates self-efficacy and anxiety as analytical categories, the study employed an explanatory sequential design (Cresswell & Cresswell, 2018).
Quantitative results capture student's use of artificial intelligence for language learning and their preferences for curricular integration. Qualitative findings provide contextual depth, showing that students use artificial intelligence strategically while negotiating concerns related to accuracy, learning transfer, course level, and instructor communication.
These findings have two major outcomes. First, they inform the development of a Template for a Transparency Statement on the Use of Artificial Intelligence for both instructors and students. Second, they shape the Language Center’s professional development workshops planned for 2026.
In this presentation, we will share the template and discuss the importance of establishing clear policies regarding artificial intelligence in language courses. We will encourage ongoing dialogue between instructors and students about the role of artificial intelligence in language development and highlight ways to leverage tools such as custom GPT models to provide scaffolding and differentiated support for learners at varied proficiency levels. By prioritizing these efforts, institutions can develop a sustainable framework for intentional and well-informed use of artificial intelligence that advances language learning outcomes.
References
ACTFL, Language Connects: “HOUR OF AI 2025 Activity Plan: AI Interchange – AI Conversation for World Language Students”, https://www.actfl.org/educator-resources/resources/ai-interchange, Retrieved, January 7th, 2026.
Chen, D., Liu, W., & Liu, X. (2024). What drives college students to use AI for L2 learning? Modeling the roles of self-efficacy, anxiety, and attitude based on an extended technology acceptance model. Acta Psychologica, 249, 104442. https://doi.org/10.1016/j.actpsy.2024.104442
Creswell, J. W., & Creswell, J. D. (2018). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches.
Hellmich, E. A., Vinall, K., Brandt, Z. M., Chen, S., & Sparks, M. M. (2024). ChatGPT in language education: Centering learner voices. Technology in Language Teaching & Learning, 6(3), 1741. https://doi.org/10.29140/tltl.v6n3.1741
Gallant, T. B., & Rettinger, D. A. (2025). The opposite of cheating: Teaching for Integrity in the Age of AI Volume 4.
Liu, J., & Ma, Q. (2025). Supporting low-proficiency L2 learners’ vocabulary learning with custom GPT-scaffolded corpus-based language pedagogy: a case study. Computer Assisted Language Learning, 1–37. https://doi.org/10.1080/09588221.2025.2539152
Mah, C., Walker, H., Phalen, L., Levine, S., Beck, S. W., & Pittman, J. (2024). Beyond CheatBots: Examining Tensions in Teachers’ and Students’ Perceptions of Cheating and Learning with ChatGPT. Education Sciences, 14(5), 500. https://doi.org/10.3390/educsci14050500
