Artificial Intelligence (AI) is transforming higher education, but its benefits can vary depending on where, how, and how often it supports learning. While prior research emphasizes cognitive and academic outcomes, this study examines how AI chatbots support the psychological needs and motivational states of engineering students. A survey of college engineering students (n = 206) examined perceived effects of AI chatbots on autonomy, relatedness, and relief from competence frustration. Structural equation modeling with latent interaction effects examined how baseline autonomy, competence frustration, relatedness, and personal agency contributed to perceived AI outcomes. Results indicate that students perceived that AI provided the greatest benefits as relief from competence frustration, smaller benefits for autonomy, and the weakest benefits for relatedness. Baseline motivational states mattered more than demographic factors, and inattention moderated how baseline competence frustration and autonomy related to perceived AI-related benefits. These results offer insights into formulating design principles for engineering-specific AI-based tools.
These findings provide theoretical support for SCT in technology‐mediated learning and suggest practical strategies for educators, including tailoring instructional design to learners' cognitive profiles, fostering motivation and self‐efficacy and leveraging AI tools to enhance active and sustained engagement in language learning.
Yifan Wang, Ran Zhi· European Journal of Educatio...· 0 citations
The skill-based dimensions of AI literacy were positively associated with AI dependency, whereas AI self-efficacy and academic self-efficacy were both negatively associated, suggesting a unified compensatory self-efficacy mechanism.
It is argued that AI self-efficacy, characterized as a student’s confidence in their capacity to employ AI ethically and efficiently, constitutes a substantial determinant and the focus of instruction should transition from a “one-size-fits-all” approach to tailored interventions designed to enhance student empowerment.
H. Herianto, Eko Wahyudi, Andi Jusmiana et al.· Knowledge Management & E...· 0 citations
Results indicate a significant level of AI usage among students, primarily for content management and academic support and the importance of integrating ethical considerations and promoting digital literacy in AI-enabled learning environments.
Christina Costa, Martha Almendarez Langland, Jason Roberson et al.· The Journal of Scholarship o...· 0 citations
Although generative AI is increasingly integrated into higher education, its impact on student’s learning experience remains unclear. This study examined factors predicting learning interactions in EFL (English as a Foreign Language) contexts, focusing on student’s AI competency, attitudes, and experience. Grounded in constructivist theory and the WEST model (Will, Experience, Skill, and Tools), a questionnaire was administered to 884 students at a Chinese higher vocational college. Structural equation modeling shows that AI integration and involvement in creative tasks directly predict learning interaction, while competency, attitudes, and experience exert indirect effects via these variables. Theoretically, this study provides quantitative evidence that student’s AI-related characteristics may contribute to learning interaction through AI-supported learning practices and creative task involvement. In practice, students can be more active in classroom interactions by adopting AI tools and participating in appropriate creative tasks designed by their teachers. Consequently, teachers play an important role in determining how AI tools are integrated and what types of tasks are assigned to students in the English classroom to support a better interactive learning environment.
Generative AI has been found, and will likely be found increasingly, useful in education. However, existing AI-for-education studies provide inconsistent evidence on its average effects. More broadly, research on prior educational technologies shows that average effects often mask substantial heterogeneity across student populations. Motivated by this evidence, this study examines heterogeneity in students'learning behavior with AI, which students benefit from AI assistance, and how learner profiles and learning behavior shape these patterns. To this end, we recruited 318 university students to participate in structured learning experiments lasting up to 125 minutes. Our findings indicate that students'learning behavior is strongly associated with learning outcomes, with behaviors characterized by proactive and critical engagement, rather than limited engagement, associated with significantly better performance. These behavioral differences are related to learner profiles, with students from higher-ranking universities and those with greater prior knowledge tending to benefit more, consistent with their greater likelihood of adopting proactive interaction strategies. Accounting for learning behavior substantially weakens or eliminates the associations between learner profiles and learning outcomes, suggesting that how students use AI is a key pathway through which background differences are linked to learning gains. Overall, this work provides a deeper understanding of AI assistance in education by showing how differences in learner profiles and learning behavior shape who benefits from AI-supported learning. These insights can help educators and students better navigate and integrate AI into educational practices.
Jingwei Yi, Yueqi Xie, Jiyan He et al.· 0 citations
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