Aug 2026· International Journal of Education and Management Engineering· Vol 16, pp. 106-127· 0 citations
TL;DR
The results demonstrate that service experience is the most influential antecedent of both emotional and behavioural outcomes, whereas the effects of functional features and information quality vary across the examined relationships.
Abstract
This study investigates the impact of AI-powered digital assistants on students’ feelings, engagement, and academic success within higher education environments. The study aims to investigate post-adoption behaviour, emphasizing how service experiences, functional attributes, information quality, and ease of interaction influence emotional and behavioural results. A structured survey was used to gather data from 431 respondents in higher education at Exploits university, Malawi, and the study utilized a quantitative approach. Measurement scales were adapted from validated studies in the AI adoption and educational technology literature and contextualized for the higher education setting. Partial Least Squares Structural Equation Modelling (PLS-SEM) version 4.1.1.8 was utilized to examine the connections between variables. Common method bias was assessed using the full collinearity approach, and all VIF values were below the recommended threshold, indicating that common method bias was not a significant concern. The results indicate that Service experience leads to Positive emotions (β = 0.205, p = 0.002) and Student engagement (β = 0.242, p < 0.001), validating H1a and H1b. Quality of information → Positive feelings (β = 0.161, p = 0.005), backing H3a, whereas Functional characteristics → Student involvement (β = 0.409, p < 0.001), supporting H4b. Positive emotions → Student involvement (β = 0.190, p < 0.001) and Academic achievement (β = 0.460, p < 0.001), and Student involvement → Academic achievement (β = 0.310, p < 0.001), confirming H5–H7. Contextualization × Positive emotions → Academic performance was noteworthy (β = 0.063, p = 0.038), reinforcing H8a. Nonetheless, H2a, H2b, H3b, H4a, and H8b received no support (p > 0.05). The research advances theoretical understanding by broadening AI adoption literature to include emotional and behavioural effects, while also enhancing practical implications by highlighting service quality and system efficiency. Suggestions emphasize the importance of focusing on contextual, high-quality AI resources to enhance student engagement, emotional well-being, and educational achievement. The results demonstrate that service experience is the most influential antecedent of both emotional and behavioural outcomes, whereas the effects of functional features and information quality vary across the examined relationships.
Artificial Intelligence (AI) is increasingly integrated into students’ academic and non-academic lives. The paper examines the predictors of student satisfaction with AI-based tools based on technological, ethical, and experiential aspects in Bangladesh, addressing a significant gap in evidence from developing countries. The quantitative research design was used and data were collected from 407 students through structured online questionnaires. The effect of trustworthiness, quality information, ease of use, service quality, security, creativity, time-saving, and academic engagement on overall satisfaction was analyzed through logistic regression analysis, adjusting the demographic and purpose-of-use factors. Trustworthiness, information reliability, ease of use, time efficiency and academic engagement significantly influenced satisfaction, highlighting the importance of ethical trust and psychological engagement while demographic characteristic had limited effect. The findings provide novel evidence from Bangladesh and offer practical guidance for educators, developers, and policymakers to promote responsible and effective AI adoption in education.
This study investigates the influence of cognitive, affective, behavioural, and learning approach factors on student engagement in mathematics among higher education students. A total of 342 students enrolled in the Business Mathematics (MAT112) course at Universiti Teknologi MARA Cawangan Terengganu participated in the study. Data were collected using a 32-item questionnaire adapted from previous studies, measured on a five-point Likert scale. The instrument demonstrated good to very good internal consistency with Cronbach's alpha values ranging from 0.767 to 0.852. Data were analysed using Pearson correlation, independent samples t-test, one-way ANOVA, and multiple linear regression. The results revealed significant positive relationships between cognitive, affective, behavioural, learning approach, and student engagement. Female students demonstrated significantly higher engagement than male students, and engagement differed significantly across academic programmes. Multiple linear regression analysis identified affective and behavioural factors as the most influential predictors, followed by cognitive and learning approach factors. The regression model explained 64.1% of the variance in student engagement, indicating good explanatory power. These findings suggest that fostering positive attitudes, active participation, and effective learning approaches may enhance student engagement in mathematics across diverse student groups.
Nor Aini binti Hassanuddin, Z. Libasin, N. Abdullah et al.· International journal of res...· 0 citations
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
The growing adoption of digital technologies in the educational realms has been focusing research interests on technology-enhanced learning environments. Although researchers agree on the existence of causal effects resulting from technology adoption in the educational milieu, the relationship between key user-technology attributes and learning outcomes remains largely understudied. This study, therefore, sought to investigate the potential human-technology correlates of distance learners’ affective and cognitive outcomes in the context of technology-mediated learning domains. The cross-sectional survey research design was used. A four-section New Media Environment Survey (NMES) battery and a 15-item multiple-choice questions aptitude test were used to collect data. Correlation analysis was used to analyse the study’s data. Results indicated statistically significant positive relationships between learner-technology interaction and affective outcomes (r (534) = 0.61, ρ = 0.000), learner-technology interaction and cognitive outcomes (r (534) = 0.10, ρ = 0.018), technology affinity and affective outcomes (r (534) = 0.56, ρ = 0.000), technology affinity and cognitive outcomes (r (534) = 0.17, ρ = 0.000), access to technology and affective outcomes (r (534) = 0.31, ρ = 0.000) and access to technology and cognitive outcomes (r (534) = 0.27, ρ = 0.000). In addition, the results revealed that while learner-technology interaction and technology affinity were correlates of affective outcomes, only access to technology was found to be a correlate of cognitive outcomes. The study recommended a structured model for orchestrating the elements that align students’ human-technology attributes with the affective and cognitive dimensions of learning outcomes.
Adeyinka Olumuyiwa Osunwusi· Journal of Education Method...· 0 citations
This study aims to analyse the effects of learner autonomy, emotional intelligence, and student engagement on academic self-concept through the mediation of e-learning use amongst university students in Malang City, Indonesia. A quantitative approach with a cross-sectional survey design was employed on 944 students from 16 universities in Malang City, which are selected using purposive sampling. Data were collected using a closed questionnaire with a five-point Likert scale and analysed using ordinary least squares-based path analysis with four classical assumption tests. Results indicate that learner autonomy significantly affects e-learning use (β = .213, p < .001) and academic self-concept (β = .299, p < .001); emotional intelligence significantly affects e-learning use (β = .185, p < .001); student engagement is the strongest predictor of e-learning use (β = .348, p < .001) and directly affects academic self-concept (β = .311, p < .001); and e-learning use significantly affects academic self-concept (β = .282, p < .001). The main finding and novelty of this study is that emotional intelligence does not have a direct effect on academic self-concept (β = .011, p = .680), but exerts a significant and exclusive indirect effect through e-learning use (β = .052). The refined model yields R² = .559. This finding confirms e-learning as a psychological mediator that converts students’ emotional capacity into academic self-concept, which is a contribution that extends emotional intelligence theory in the context of digital higher education.
Jozua Ferjanus Palandi, Mardji, Eddy Sutadji et al.· Letters in Information Techn...· 0 citations
This study aimed to explore the correlation between the construct of digital self-efficacy and engagement of students in the context of AI-driven learning environments, while also investigating the potential mediation of academic motivation. The design applied was a descriptive correlational cross-sectional design. The study included a sample of 130 undergraduate students at Al Esraa University who had experience in using AI tools to help in the academic process. A 24-item questionnaire, assessed through three constructs - digital self-efficacy, academic motivation, and student engagement, was used for data collection. Means, standard deviations, Pearson's correlation coefficients, and mediation analysis using Hayes' PROCESS Macro, Model 4, were employed to analyze the data. The results indicated that students possessed high digital self-efficacy and were highly motivated and engaged in learning environments supported by AI. Academic motivation was the highest and positively significant with student engagement, and digital self-efficacy was positively and significantly associated with academic motivation and student engagement. The mediation analysis revealed that digital self-efficacy was partially mediated by academic motivation. The findings pointed out that students' confidence with the use of digital and AI-supported tools had a direct influence on the engagement of students, but also had an indirect effect through strengthening the academic motivation of the students. Overall, the study suggested steps to be implemented to enhance students' confidence, critical evaluation, and motivation while fostering meaningful and responsible interaction with the use of AI in their learning process.
Linda Ahmad Khateeb, R. Freihat· Journal of Ecohumanism· 0 citations
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