Table 2.

Variables and problems.

Variable	Item	References
Social influence	1. My classmates/friends have a positive attitude toward AIGC technology.	Abdaljaleel et al. (2024)
	2. I feel supported by my peers, which enhances my expectations of AIGC technology.
	3. In my social circle, the use of AIGC technology is considered beneficial.
Hedonic motivation	1. I believe using AIGC technology can provide an enjoyable learning experience.	Abdaljaleel et al. (2024)
	2. I look forward to using AIGC technology to gain enjoyment in my learning.
	3. I have a high level of interest in using such technology.
Anthropomorphism	1. I believe that if AIGC technology is designed to be more human-like, it would enhance my learning outcomes.	Zhang and Rau (2023)
	2. AIGC technology with human-like characteristics raises my expectations of its performance.
	3. I am more optimistic about the results of using a personified AIGC system.
Perceived ethical risks	1. I am concerned that using AIGC technology may raise ethical issues.	Stahl and Eke (2024)
	2. My perception of the ethical risks of AIGC technology affects my expectations of its effectiveness.
	3. If I have concerns about the ethical risks of AIGC technology, I will lower my expectations of it.
Algorithmic interpretability	1. If I can understand how AIGC technology works, I would trust its output more.	Chen (2024)
	2. Clear algorithmic interpretability raises my expectations for the effectiveness of AIGC technology.
	3. I hope AIGC technology will provide transparent operation and result explanations.
Generation quality	1. I expect AIGC technology to deliver high-quality results.	Sančanin and Penjišević (2022)
	2. I believe the quality of generated content directly impacts my learning outcomes.
	3. High-quality outputs will enhance my expectations for using AIGC technology.
Context-awareness	1. AIGC technology can provide targeted assistance based on my learning context.	Augusto (2022)
	2. I expect AIGC technology to understand and adapt to my learning needs.
	3. AIGC technology with strong Context-awareness makes me more confident in its effectiveness.
Performance expectancy	1. I believe AIGC technology can improve my learning efficiency.	Elliot et al. (2021)
	2. I am confident in the effectiveness of AIGC technology.
	3. I expect that using AIGC technology will lead to significant learning outcomes.
Effort expectancy	1. I feel that using AIGC technology will be an easy and pleasant experience.	Cao and Niu (2019)
	2. The experience of using AIGC technology makes me willing to put in more effort.
	3. I am willing to invest time and effort to learn AIGC technology.
Emotions	1. I feel excited and positive when using AIGC technology.	Venkatesh et al. (2003)
	2. The emotions I experience while using AIGC technology are pleasant.
	3. I am satisfied with my experience using AIGC technology.
Willingness to use	1. I am willing to try using AIGC technology for learning.	Gursoy et al. (2019)
	2. I hope to continue using AIGC technology in the future.
	3. I would recommend AIGC technology to my peers.