Jul 2026· European Journal of Education Studies· Vol 13· 0 citations· 5 references
TL;DR
Findings indicate a significant positive relationship between AI-powered adaptive learning and students’ academic achievement and engagement and technology acceptance (UTAUT factors) significantly influenced students’ learning outcomes.
Abstract
This study examines the impact of AI-powered adaptive learning on students’ academic achievement and engagement in secondary schools in Nigeria. It also explores the moderating effects of gender and technology acceptance using the Unified Theory of Acceptance and Use of Technology (UTAUT) framework. A quantitative survey research design was adopted, and data were collected from secondary school students in private schools with access to the uLesson AI adaptive learning tool. A structured questionnaire, consisting of the Adaptive Learning Engagement Scale (ALES) (r = .75) and the Technology Acceptance and Academic Performance Scale (r =.78), was used to gather responses. Descriptive and inferential statistics, including multiple regression analysis, were employed to analyze the data. Findings indicate a significant positive relationship between AI-powered adaptive learning and students’ academic achievement and engagement. Also, technology acceptance (UTAUT factors) significantly influenced students’ learning outcomes, with performance expectancy and effort expectancy emerging as key predictors. While gender had a notable effect on academic performance, its impact on engagement was not statistically significant. The study highlights the importance of integrating AI-driven learning tools with Supportive technology acceptance strategies to enhance student outcomes. It is recommended that educators and policymakers promote AI adoption, provide necessary training, and develop policies that foster a technology-friendly learning environment in Nigerian secondary schools.
The integration of artificial intelligence (AI) in higher education has provided the opportunity to bring new possibilities into the realm of adaptive and data-driven learning environments. The aim of this study is to explore the impact of AI gamification on inclusive learning outcomes in higher education and the role of learner engagement, learning motivation, and personalized learning experience in this relationship. The data were collected using a quantitative, cross-sectional survey design from 303 students from higher education institutions in India, who reported prior experience of AI-based or gamified learning platforms. IBM AMOS was used for the Structural Equation Modeling (SEM). AIG had significant positive impacts on learner engagement (
β
= 0.572,
p
< 0.001), learning motivation (
β
= 0.483,
p
< 0.001), and personalized learning experience (
β
= 0.566,
p
< 0.001). Inclusive learning outcomes were significantly predicted by all three mediating variables. Learning motivation emerged as the strongest predictor (
β
= 0.466,
p
< 0.001), followed by personalized learning experience (
β
= 0.455,
p
< 0.001) and learner engagement (
β
= 0.388,
p
< 0.001). A significant negative direct effect was found between AIG and inclusive learning outcomes (
β
= −0.256,
p
< 0.001), indicating a suppression effect: the net positive effect of AIG on inclusive learning outcomes is entirely mediated through the three psychological and experiential pathways. Bootstrapped mediation analysis (2,000 resamples) confirmed significant partial mediation via learning motivation (
β
indirect = 0.199, 95% CI [0.132–0.268]), personalized learning experience (
β
indirect = 0.198, 95% CI [0.129–0.261]), and learner engagement (
β
indirect = 0.176, 95% CI [0.115–0.241]). Prior online learning experience had a significant positive effect (
β
= 0.105,
p
= 0.004), and digital literacy had a significant positive effect (
β
= 0.138,
p
< 0.001) on inclusive learning outcomes. Model fit was acceptable:
χ
2
/df = 2.384, RMSEA = 0.069, CFI = 0.896, TLI = 0.883, IFI = 0.897. Incremental fit indices are marginally below the conventional 0.90 threshold, though
χ
2
/df and RMSEA meet accepted benchmarks; effect sizes should be interpreted with appropriate caution. This study extends Self-Determination Theory by operationalizing AI-driven gamification (AIG) as a single adaptive construct within a SEM mediation model, revealing a suppression effect in which AIG promotes inclusive learning outcomes exclusively through engagement, motivation, and personalization pathways. The findings offer actionable guidance for platform designers, educators, and policymakers committed to building equitable AI-enhanced learning environments in higher education.
Prashanth Beleya, N. Chauhan, K. P. Jaheer Mukthar et al.· Frontiers of Computer Scienc...· 0 citations
Academic learning has been transformed since the integration of Artificial Intelligence (AI) in education, offering opportunities for students’ development. This study aims to examine the effect of AI-powered tools on academic performance, conducted among students at the Faculty of Education at Ajilat city. Also, the analysis explores the potential benefits and challenges of AI technology in education. Data were collected using Google Forms via a 39-item questionnaire, as follows: 20 questions assessing AI-powered tools and 19 questions measuring academic student performance. The total number of respondents was 175 male and female students. The data analysis was conducted using the SPSS program and Smart Pls software. Quantitative data were analyzed using frequency and percentage (demographic variables) calculations by SPSS. The percentage of males was 16 (9.1%), and the percentage of females was 159 (90.9%). Descriptive analysis for academic performance and AI-powered tools showed that both of them had a positive effect on their studies, represented respectively as (3.53) and (3.67). Quantitative responses were subjected to thematic analysis using smart pls (SEM). This allows us to analyze both direct effects and test if relationships are linear or nonlinear (between the dependent variable and independent variable); there was a significant, large positive relationship between them, R2 (50%) and Beta (0.709). Also, evaluate the relationship's strength and significance through indicators like outer loadings, path coefficients, and Average Variance Extracted (AVE), as explained by CA (0.945) and (0.972) and AVE (0.567) and (0.683).
Awatef Saad· AlQalam journal of medical a...· 0 citations
Artificial Intelligence (AI) is a transformative technology reshaping teaching and learning practices. By enabling personalized, cost-effective, and adaptive learning experiences, AI has become increasingly integrated into educational environments. However, its adoption also raises concerns related to credibility, trustworthiness, accuracy, privacy, and security. As part of a larger research project, this paper examines students' perceptions of AI-based personalised learning tools in Malaysian higher education institutions. Student perceptions were investigated using three variables derived from the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2): Performance Expectancy (PE), Effort Expectancy (EE), and Social Influence (SI), with an additional variable, Reliability and Trust (RT). A qualitative study involving in-depth interviews with thirteen participants was conducted to explore their experiences with an AI-based personalised learning tool, Brainly. Using thematic analysis, five themes were identified: support and customization, learning efficiency, accessibility and interactivity, peer encouragement and social comparison, and credibility and dependability. Learning efficiency emerged as the most prominent theme, with 92% of participants reporting enhanced understanding of complex topics, improved learning effectiveness through accessibility, immediate feedback, and interactive learning, and better efficiency in revision and practice. The findings provide insights for developers, academic institutions, and policymakers regarding AI personalised learning tool selection, syllabus development, and integration strategies.
Noor Hafizah Hassan, Cheryssa Wong Kai Ying, P. Govindasamy et al.· Edelweiss Applied Science an...· 0 citations
Adaptive learning systems (with the role of artificial intelligence, AI) are increasingly becoming a part of higher education to provide personalization of the content, dynamic learning pathways, and timely feedback on student progress. This paper investigates the effect of cognitive engagement, academic performance and self-regulated learning in adopting AI-based adaptive learning systems among students in a Sri Lankan university. The analysis relies on the theory of educational technology adoption and learner-centered theory, the study examines the possibility that the learning behaviors and learning attributes of students influence their adoption and use of AI-enhanced learning conditions. The survey data involving 100 students at a university was used in a quantitative cross-sectional study that was conducted to collect information on the topics of AI-based learning tools exposure and its effect on the students. The analysis of the data was carried out through descriptive statistics, reliability analysis, Pearson correlation, and multiple regression of SPSS. The results indicated that AI adoption had a significant and positive effect on self-regulated learning and academic performance as opposed to cognitive engagement which was found to have a less significant effect on AI adoption in the regression model. The model explains 48.1% of AI adoption, showing that self-directed learning ability and learning orientation influence AI use more than engagement in higher education.
Hiranthika Madumali Chandrasena Hiranthika· Critical Journal of Social S...· 0 citations
The rapid integration of artificial intelligence (AI) into higher education is reshaping how university students access information, engage with learning, and develop academic competencies. However, limited empirical research has examined AI-augmented cognition as a broader educational phenomenon connecting students’ cognitive use of AI with both academic and practical outcomes. This study examined the relationship of AI-augmented cognition with financial awareness, learning engagement, and students’ academic development among university students in Pakistan. A quantitative cross-sectional research design was adopted, and data were collected from 467 university students enrolled in higher education institutions across Punjab and Sindh, Pakistan. An adapted structured questionnaire containing established measures of AI-augmented cognition, financial awareness, learning engagement, and academic development was administered through Google Forms, and the survey link was distributed electronically to eligible respondents through academic and student networks. The collected responses were organized and screened before being analyzed quantitatively to examine relationships, predictive patterns, and differences among relevant student groups. The findings revealed that AI-augmented cognition was positively associated with financial awareness, learning engagement, and students’ academic development, with the strongest relationship observed with academic development. AI-augmented cognition also demonstrated a meaningful predictive contribution to all three outcomes, indicating that students who engaged more actively with AI as a cognitive resource tended to report greater financial awareness, stronger learning engagement, and enhanced academic development. Furthermore, students with previous generative AI experience demonstrated comparatively higher levels of the major study variables, while differences were also observed across varying levels of AI-use frequency. These findings highlight that the educational significance of AI extends beyond technological adoption and conventional academic assistance, suggesting that purposeful AI use may support students’ cognitive development, engagement with learning, and practical awareness of financial matters. The study contributes to the emerging literature by positioning AI as a cognitive augmentation resource rather than merely a digital learning tool and provides practical implications for higher education institutions seeking to promote AI literacy, critical evaluation, responsible AI use, and meaningful student development.
Imran Mughal, Memoona Aslam, Zahid Hussain Sahito· Journal of Global Social Tra...· 0 citations
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