Jul 2026· Frontiers of Computer Science· 0 citations· 35 references
TL;DR
A suppression effect is revealed in which AIG promotes inclusive learning outcomes exclusively through engagement, motivation, and personalization pathways, revealing a suppression effect in which AIG promotes inclusive learning outcomes exclusively through engagement, motivation, and personalization pathways.
Abstract
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.
Abstrak: The transformation of 21st-century education demands learning that accommodates differences in learning styles, adaptive needs, and improves student learning outcomes. The use of Artificial Intelligence (AI) and gamification in learning media offers an innovative approach that not only increases learning motivation but also strengthens critical and collaborative thinking processes. This study aims to analyze how the development of AI-based learning media and gamification contributes to improving student learning outcomes. The research method used a Systematic Literature Review (SLR) of 20 indexed scientific articles from 2022–2025 through the Consensus platform. The results show that AI integration supports learning personalization and automated evaluation, while gamification creates an interactive and challenging learning environment. Empirically, the combination of the two improves student learning outcomes by an average of 35–45% compared to conventional methods. These results emphasize the importance of collaboration between technological innovation and adaptive pedagogy to build an inclusive and sustainable digital education ecosystem.
Moh. Mazhud Hasyim, Moh. Dafid, M.Ali Zainal Abidin et al.· Journal on Research and Revi...· 0 citations
Findings show that the use of AImediates the relationship between DT and students’ behavioural and academic outcomes, and shows that the use of AI helps shape student experiences and outcomes.
N. Shaya, Rawan Abukhait, Muhammad Nisar Khattak et al.· Journal of Applied Learning...· 0 citations
Background The increasing adoption of artificial intelligence and learning analytics has accelerated the transformation of vocational education toward more adaptive and data-informed learning environments. However, existing approaches are often implemented as isolated technologies rather than integrated learning ecosystems. This study developed and evaluated a SmartFlex Learning Ecosystem that combines artificial intelligence, learning analytics, and flexible learning design to support vocational mathematics education. Methods A Design-Based Research approach was employed through four iterative phases: analysis, design, implementation, and evaluation. The SmartFlex Learning Ecosystem was implemented over a 12-week period involving 172 vocational students. Quantitative data were collected through structured questionnaires and learning analytics records, while system effectiveness was examined using Partial Least Squares Structural Equation Modeling and implementation-based learning analytics. Results The findings indicate that the SmartFlex Learning Ecosystem positively influenced learning engagement (β = 0.62, p < 0.001) and adaptive learning (β = 0.58, p < 0.001). Artificial intelligence-driven personalization showed a strong association with adaptive learning (β = 0.65), while learning analytics capability contributed to learning engagement (β = 0.53). Learning engagement (β = 0.61) and adaptive learning (β = 0.64) were positively related to mathematical competence, which subsequently influenced vocational skill readiness (β = 0.67). The model demonstrated substantial explanatory power (R
2
= 0.71) and predictive relevance (Q
2
= 0.52). Implementation data further showed an increase in login frequency of 142% and an improvement in assignment completion rates from 64% to 88%. Conclusions The SmartFlex Learning Ecosystem demonstrates the potential of integrating artificial intelligence, learning analytics, and flexible learning strategies within vocational mathematics education. The findings suggest that data-informed and adaptive learning environments can support student engagement, mathematical competence, and vocational skill readiness while providing a framework for future development of technology-enhanced vocational learning systems.
In the era of digital transformation, higher education institutions are producing enormous amounts of students' data, but many of them still do not fully utilize that data for educational insight to the benefit of student success. This gap is filled by this study developing and testing a theoretically informed AI-supported learning analytics framework in a pedagogically relevant way that goes beyond algorithmic optimization. The research design used was quantitative with a predictive correlational approach in which 1248 students from various institutes of higher education in Pakistan and abroad were involved. Data consists of LMS interaction logs, attendance records, assessment results, and self-regulation surveys that were validated. The proposed framework was tested using descriptive statistics, structural equation modeling and machine learning approaches for analyzing data. Results showed good predictive accuracy (82.4% for the identification of the at-risk students), and that self-regulated learning behaviors proved to be important mediators. The proportion of children with cycling, walking and active play increased significantly using AI-generated indicators, but demographic discrepancies remained. Practically, it recommends the universities how to implement in a responsible way that can support the value of teaching, student support, institutional decision-making, and ethical norms.
N. Aslam, Aniqa Naz, M. Nadeem et al.· Journal of Language, Literat...· 0 citations
Post-pandemic digital transformation has accelerated the adoption of Learning Management Systems (LMS) and hybrid learning models, shifting the key issue from technology availability to acceptance, use, and sustained engagement. The emergence of AI-enhanced LMS—integrating generative AI and deep learning analytics—introduces both opportunities and risks that affect user adoption. This study examines acceptance and use of NotebookLM integrated with Google Classroom at Universitas Negeri Semarang using the Unified Theory of Acceptance and Use of Technology (UTAUT) framework extended with AI Literacy as a moderating variable. A quantitative approach using Partial Least Squares Structural Equation Modelling (PLS-SEM) was applied to data collected from 83 students of the Civil Engineering Education study programme. Results show that six of seven hypotheses were supported: Performance Expectancy (β = 0.433), Effort Expectancy (β = 0.512), Social Influence (β= 0.590), and Facilitating Conditions (β = 0.441) significantly predicted Behavioral Intention; Facilitating Conditions (β = 0.526) and Behavioral Intention (β = 0.763) significantly predicted Use Behavior. Social Influence emerged as the strongest predictor of Behavioral Intention. AI Literacy did not moderate the Effort Expectancy–Behavioral Intention relationship but acted as an independent predictor. Findings suggest that institutional social climate and instructor support are critical drivers for AI-enhanced LMS adoption in vocational engineering education.
Bambang Sugiyarto, Virgiawan Adi Kristianto, Sucipto et al.· MATEC Web of Conferences· 0 citations
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