Abstract:

This paper delves into the topic of student acceptance of AI-based feedback systems in the context of self-regulated learning in higher education.

The use of AI in education has been growing rapidly, mainly due to OpenAI's ChatGPT, which employs AI to communicate with users via text messages. The impact of AI-based systems like ChatGPT on education has been widely discussed.

Notwithstanding this recent discussion, AI-supported systems have been a subject of educational research for a long time. However, a systematic review of AI in higher education from 2016 to 2022 has revealed a significant increase in publications in 2021 and 2022. The review has identified five areas where AI is used:
(1) assessment/evaluation,
(2) prediction,
(3) AI assistant,
(4) Intelligent Tutoring System (ITS), and
(5) student learning management.

Therefore, the development of personalized and precise automated feedback systems for students is a significant area for student support in the learning process by AI (Xu et al., 2023).

However, recent empirical studies on students' acceptance of the integration of AI in their teaching and learning context have provided a mixed picture. Although there is general student approval for AI learning applications, this approval decreases when a concrete introduction in the individual learning context is announced (Rodway & Schepman, 2023).

Against this background, this paper aims to provide empirical evidence regarding the use of AI in higher education. A case study from Germany on student acceptance of an AI-based feedback system to support self-regulated learning in voluntary mathematics courses is presented. The feedback system includes an AI component that enables autonomous and automatic checking of tasks submitted by students. The students can submit assignments as often as they like and contact tutors for additional support.

The empirical investigation aims to determine the students' acceptance of the AI-based feedback system. The Technology Acceptance Model (TAM) forms the conceptual basis for the examination. The TAM has identified two primary predictors for technology acceptance: perceived usefulness (PU) and perceived ease of use (PEOU).

The research methodology involves a mixed-method approach. A survey was created and conducted based on validated questionnaires that correspond to the TAM. Additionally, semi-structured interviews are planned with students about their user experience to be completed at the end of this year.
Preliminary results of the evaluation indicate moderately positive acceptance of the feedback system in terms of both PU and PEOU. Furthermore, students show acceptance towards the use of AI to optimise the feedback system. If these results are further confirmed during the ongoing evaluation, it would provide a stimulating empirical contribution to the question of student acceptance of AI in higher education.

References:
[1] Rodway, P., & Schepman, A. (2023). The impact of adopting AI educational technologies on projected course satisfaction in university students. Computers and Education: Artificial Intelligence, 5, 100150. https://doi.org/10.1016/j.caeai.2023.100150
[2] Xu, W., Meng, J., Raja, S. K. S., Priya, M. P., & Kiruthiga Devi, M. (2023). Artificial intelligence in constructing personalized and accurate feedback systems for students. International Journal of Modeling, Simulation, and Scientific Computing, 14(01), 2341001. https://doi.org/10.1142/S1793962323410015

Keywords:

Artificial Intelligence, Feedback Systems, Technology Acceptance Model (TAM), Higher Education, Self-regulated Learning.