Jun 2026· Journal of Information & Knowledge Management· 0 citations
TL;DR
The findings suggest that AI-supported instruction provides additional explanatory value beyond engagement alone and that its effectiveness depends on coherent pedagogical integration.
Abstract
Artificial intelligence (AI) is increasingly being integrated into higher education, yet its educational value depends not only on access to technological tools but also on how these tools are pedagogically embedded within teaching and learning. Although prior studies have reported positive associations between educational technologies and learning outcomes, fewer studies have examined whether AI tool usage explains perceived learning effectiveness beyond established engagement dimensions and instructional integration quality. This study investigates the relationships among AI tool usage, behavioural, cognitive and emotional engagement, instructional integration quality, and perceived learning effectiveness in higher education. Using a quantitative cross-sectional survey design, data were collected from 214 participants, including faculty members and undergraduate students, through a structured Likert-scale questionnaire. Descriptive, correlational, regression and group-comparison analyses were conducted to examine associative and predictive patterns. The findings indicate that AI tool usage was positively associated with all engagement dimensions and with perceived learning effectiveness. In the regression model, AI tool usage remained a significant independent predictor of perceived learning effectiveness even after controlling for engagement dimensions and instructional integration quality, while instructional integration quality also showed a significant positive effect. In addition, AI users reported significantly higher levels of engagement and learning effectiveness than non-users, whereas no significant differences were observed between faculty and students in engagement dimensions or learning outcomes. These findings suggest that AI-supported instruction provides additional explanatory value beyond engagement alone and that its effectiveness depends on coherent pedagogical integration. The study contributes to the AI-in-education literature by offering empirical evidence on the role of AI-supported instruction within an engagement-based framework and by highlighting implications for instructional design, faculty development, and institutional AI integration in higher education.
The findings suggest that competence-related experiences may represent an important psychological pathway linking AI-supported teaching and student engagement in structured vocational education contexts and the non-significant role of perceived autonomy indicates that motivational processes in AI-supported learning may vary across instructional settings and should be interpreted in relation to contextual and pedagogical conditions.
The findings suggest that student engagement in AI‐assisted learning is linked not only to the availability of technological and interpersonal support, but also to the extent to which students experience learning as focused, absorbing and meaningful.
Ping Xu, Yongbi 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.
This study used a convergent parallel mixed-methods design to examine at how generative Artificial Intelligence (AI) is reshaping educational practices, however there is limited evidence regarding its impact on students’ learning outcomes, especially in developing countries. This study further examined the relationships among AI-related skills, perceived usefulness, self-efficacy, motivation, and academic performance, as well as students’ experiences with generative AI in learning environments. Applying a survey design, this research collected survey data from two hundred ninety-nine ( N = 299) students in Nepal, Indonesia, and Brazil. This study also conducted semi-structured interviews with fourteen ( N = 14) interviewees. Quantitative results indicated low levels of AI literacy, self-efficacy, perceived usefulness, and motivation, all averaging means below midpoint scores of five point Likert scale of survey variables. Regression analyses revealed weak correlations between those independent variables and reported academic enhancement, suggesting that students often lack the confidence and skills required to integrate AI tools effectively into their learning activities.
Conversely, qualitative results highlighted significant advantages of generative AI, such as improved learning efficiency, enhanced communication, and greater problem-solving capabilities. Interviewees noted that AI tools simplified complex concepts and saved time, despite receiving limited formal training. The integration of quantitative and qualitative results exposed a perception practice gap: while students benefited from generative AI, they generally underestimated their abilities and lacked structured guidance. The study concludes that successful AI integration in education required more than mere access to technology; it requires fostering AI literacy, self-efficacy, motivation, and ethical awareness through dedicated pedagogical support.
Basanta Prasad Adhikari, Suyantiningsih, Ariyawan Agung Nugroho et al.· OCEM Journal of Management,...· 0 citations
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.
The increasing rate of embracing artificial intelligence (AI) in higher institutions of learning globally has generated significant debate concerning its impact on student engagement and academic performance. While AI-powered tools promise personalized learning and greater efficiency, their effectiveness is highly dependent upon several moderating factors. An in-depth systematic review was deplored to investigate the relationship between AI-powered learning tools, student engagement, and academic performance, focusing on the moderating roles of perceived ease of use (PEOU), frequency of AI tool use, instructor training and support (IT&S), and student digital literacy levels. The detailed review synthesized existing literature to address vital research questions, exploring how these moderating factors influence the technology's effectiveness. The findings confirmed PEOU as the most significant predictor of positive student attitudes, while a higher frequency of AI tool use consistently correlates with better engagement and academic outcomes. Crucially,IT&S serves as the indispensable bridge, enabling instructors transition to studentcentred models and effectively manage AI's ethical and logistical demands. However, the in-depth review suggests that digital literacy plays a complex, but an indirect role: though it is necessary for technical competence, it does not automatically ensure greater academic engagement, suggesting a persistent need for motivationalapproaches. These findings highlight the need for balanced AI incorporation that prioritize both academic success and student well-being. This review contributes key insights for policymakers and developers seeking to enhance AI-powered learning environments
Abioladun Olumide Ayeni, Ezekiel Etu Achena, Samuel Tobi Solademi et al.· Journal of Educational Revie...· 0 citations
