Aug 2026· Review of Artificial Intelligence in Education· Vol 7, pp. e01125· 0 citations· 12 references
TL;DR
A conceptual framework offering AI designers, teacher educators, and policymakers a psychologically grounded, empirically mapped, and internationally contextualised basis for developing AI-supported educational systems that advance equity, learner engagement, and meaningful educational transformation is proposed.
Abstract
Background: The rapid integration of artificial intelligence (AI) into educational systems worldwide presents significant opportunities for personalised learning, inclusive pedagogy, and data-informed instructional practice, yet AI systems designed without an understanding of how learners acquire knowledge, sustain motivation, regulate emotions, and respond to diverse social and cultural contexts may unintentionally reinforce existing educational inequalities.
Objective: This paper proposes a conceptual framework that positions educational psychology as the primary foundation, rather than a secondary consideration, for the design and evaluation of AI-supported educational systems.
Methods: The framework was developed through a systematic narrative synthesis of peer-reviewed literature, international policy documents, and recent empirical evidence, drawing on searches of PsycINFO, ERIC, Scopus, and Web of Science (2011–2025), supplemented by forward and backward citation tracking and review of international policy reports. The search yielded approximately 620 potentially relevant sources, of which approximately 65 were incorporated following title/abstract screening and full-text review against explicit inclusion criteria.
Results: The resulting framework comprises three interconnected components — a psychologically informed understanding of learner diversity, AI-enabled inclusive and innovative pedagogical practices, and reflective teaching within intelligent learning environments — mapped, through a reference table, against specific AI applications, illustrative empirical evidence, and inclusion implications. A comparative analysis across East Asia, Europe, Sub-Saharan Africa, and South Asia further identifies region-specific challenges, policy contexts, and persistent gaps in cross-cultural validation, particularly regarding generative AI and adaptive learning tools.
Conclusion: The framework offers AI designers, teacher educators, and policymakers a psychologically grounded, empirically mapped, and internationally contextualised basis for developing AI-supported educational systems that advance equity, learner engagement, and meaningful educational transformation, provided that access, teacher readiness, cultural validity, and ethical safeguards are adequately addressed.
Artificial intelligence (AI) is reshaping digital learning environments across higher education, medical training, and K-12 contexts, yet the field lacks a unifying conceptual account linking learner psychology, institutional readiness, and sustainability-oriented outcomes. This systematic literature review (SLR) synthesizes 29 peer-reviewed and conceptual papers spanning AI literacy, AI anxiety, self-regulated learning (SRL), human-AI delegation, algorithmic transparency, and AI-driven institutional transformation. Using a PRISMA-informed selection process, studies were screened for empirical or conceptual relevance to AI in digital learning, yielding a final corpus analyzed for research gaps, objectives, methodological design, data collection approach, and key findings. Results indicate that AI literacy and perceived opportunities consistently predict acceptance and adoption, that anxiety and trust/transparency perceptions act as critical psychological mediators, and that sustainability and organizational-culture factors increasingly shape institutional AI adoption beyond the individual learner level. The review proposes an integrated conceptual framework connecting antecedents (AI literacy, perceived opportunities/challenges, institutional readiness), mediating psychological processes (anxiety, needs satisfaction, trust), and outcomes (acceptance, SRL, work engagement, sustainable innovation). Gaps remain in cross-cultural validation, longitudinal and experimental designs, and integrated governance frameworks, pointing to a research agenda relevant to dissertation work at the intersection of AI, education, and behavioral science.
Bhawna· EPRA International Journal o...· 0 citations
The use of Generative Artificial Intelligence (Gen-AI) across education, healthcare, ethics, and professional practice is rapidly increasing. This growth has sparked a polarized debate, with Gen-AI viewed either as a powerful cognitive augmenter or as a potential threat to critical thinking, creativity, and independent reasoning. To examine this issue, the review scope was defined using the PRISMA-ScR guidelines, Joanna Briggs Institute (JBI) methodology, and the Population–Concept–Context (PCC) framework. A systematic literature search was conducted across PubMed, IEEE Xplore, ACM Digital Library, Springer, and Wiley Online Library. Only English-language publications published between 2020 and 2025 were considered. Following multi-stage screening, duplicate removal, eligibility assessment, and human verification, 107 peer-reviewed articles were included in the final analysis. Among the selected studies, 42% focused on university education, 23% on school education, 16% on medical education, 11% on ethics and moral responsibilities, and 8% on professional training. Synthesis of the findings led to two major conceptual contributions. First, the Cognitive Impact Taxonomy (CIT) provides a structured approach for assessing Gen-AI’s influence on fluid and crystallized intelligence, key cognitive processes, and the trajectory of cognitive impacts leading to severity assessment. Second, the Integrated Cognitive Symbiosis Framework (ICSF) introduces a three-layer governance model to support responsible and cognitively healthy Gen-AI adoption. Together, the CIT–ICSF pipeline offers educators, institutional leaders, and policymakers a practical mechanism to maximize the benefits of Gen-AI while mitigating potential long-term cognitive risks.
K. Vidanage, M.J. Marapperuma, Shakya Dissanayake et al.· Davao Research Journal· 0 citations
The rapid integration of artificial intelligence (AI) into education has redefined the nature of competency development, yet empirical evidence remains fragmented and inconsistent across contexts. While AI interventions are often promoted for enhancing personalization, assessment, and learner engagement, uncertainty persists regarding their pedagogical authenticity, equity implications, and measurable impact on learning outcomes. This systematic literature review aimed to evaluate the effect of AIenabled instructional interventions (Intervention) on learners’ competency outcomes (Outcome) among students and educators in formal and nonformal education (Population) compared with conventional or nonAIassisted pedagogies (Comparison).Following the PRISMA 2020 reporting standard and CASP appraisal procedures, five databases (Scopus, Web of Science, ERIC, ACM Digital Library, and IEEE Xplore) were systematically searched for studies published between 2019 and 2025. Inclusion criteria encompassed empirical research where AI formed a core pedagogical, analytical, or delivery component, with sufficient data for quantitative or qualitative synthesis. Twenty studies met initial screening criteria, and ten satisfied full eligibility for inclusion in the final synthesis. Data were analyzed through a mixedmethod approach combining narrative synthesis and qualityweighted comparison. Findings reveal that AIdriven interventions particularly those employing humancentered design, multimodal analytics, and outcomebased knowledge graph mapping—significantly improved competency gains, engagement, and instructional alignment. However, disparities in institutional readiness, data integration, and ethical governance persist, constraining scalability. The review concludes that sustainable AI competency development requires harmonizing technological innovation with pedagogical integrity and equity frameworks, emphasizing the human role in guiding AImediated learning ecosystems.
Zahari Hamidon· Muallim Journal of Social Sc...· 0 citations
The rapid growth of generative Artificial Intelligence (AI) is reshaping how higher education conceptualizes learning, assessment, and pedagogy. Many institutions respond by relying on restrictive policies. Unfortunately, this approach often fails to support meaningful and sustainable education. The objective of this study is to reinterpret the Artificial Intelligence Assessment Scale (AIAS) (Perkins et al., 2024) as a developmental pedagogical framework that enables transparent, ethical, and reflective integration of AI into teaching and learning. Methodologically, the study applies the five-level AIAS model, ranging from AI prohibition to full AI collaboration, in FASH 137: Clothing, Society, and Culture, a General Education course examining the sociocultural meanings of dress. AI integration is scaffolded across multiple assignments, each explicitly aligned with a designated AIAS level. Data are drawn from assignment design, faculty observations, and structured student reflections documenting AI use and learning outcomes. Findings reveal three interrelated pedagogical themes. First, transparency as pedagogical integrity emerges through required “AI Use Notes,” which normalize disclosure and foster academic trust. Second, critical evaluation as human distinction is strengthened as students compare AI-generated insights with their own analyses, reinforcing judgment, creativity, and cultural interpretation. Third, AI as a structured learning partner supports exploration, critique, writing development, and identity reflection without replacing human authorship. The research outcomes demonstrate that AIAS functions effectively as a learning architecture, aligning academic integrity with instructional design. The framework offers a replicable model for fashion programs and other disciplines seeking responsible AI integration. Future research will expand empirical assessment across courses, disciplines, and institutions, examine longitudinal learning impacts, and refine discipline-specific AIAS applications to guide higher education in the AI-driven future.
D. Shen· PUPIL International Journal...· 0 citations
In an era of rapid generative artificial intelligence (GAI) integration into education, students are increasingly using these tools not merely as learning aids but as their primary means for completing assessments. This shift raises significant concerns regarding academic integrity, cognitive offloading, and the erosion of critical thinking. To address these challenges, this paper advances a conceptual, neuroscience-informed framework, the Lifelong Learning, Engagement, Active Processing, Reflection, and Neuro-based Design (LEARN) model, for the ethical and pedagogically grounded integration of GAI into assessment contexts. Grounded in over two decades of experience with problem-based learning (PBL), the framework emphasises learner autonomy, adaptability, and sustained cognitive engagement. The LEARN framework synthesises principles from cognitive and educational neuroscience with constructivist learning theory to explain how learning processes such as neuroplasticity, effortful cognition, metacognitive regulation, and socio-emotional engagement can be intentionally supported in AI-mediated environments. Each component positions GAI as a cognitive scaffold rather than as a cognitive substitute, encouraging critical evaluation, reflective judgement, and ethical self-regulation. By integrating neuroscience-informed learning design, PBL pedagogy, and responsible AI use, the LEARN framework contributes a theoretically grounded model for redesigning assessment practices that sustain deep, self-directed, and reflective learning in the context of generative AI.
Lorna Uden, Gwo-Jen Hwang· Journal of Computers in Educ...· 0 citations
Background: While AI is promoted as a transformative force in education through adaptive platforms and real-time feedback, its implementation for multilingual learners often risks reinforcing educational inequalities. Current scholarship cautions that automated systems still struggle with cultural nuances and idiomatic expressions, highlighting the need for design approaches that foreground equity and human agency.
Objective: This systematic review examines 10 core studies through the lens of the ISO 9241-210 Human-Centered Design framework. The objective is to analyze how AI-based educational technologies are designed and evaluated to support multilingual learners, specifically focusing on the "Context of Use," "User Requirements," "Design Solutions," and "Evaluation" phases.
Methods: Through a systematic search of four databases and subsequent snowballing, 10 core papers were selected to analyze how AI tools address linguistic and cultural diversity.
Results: Across the reviewed studies, reported improvements ranged from quantitative gains, including a 25–31% increase in literacy and vocabulary retention and a rise in academic success rates up to 77.8%. Furthermore, AI-driven systems, when aligned with HCD principles, were associated with saving educators up to 41% of their time. However, evaluations revealed critical 'socio-technical paradoxes': students faced a "trade-off" where they reverted to English-centric prompting due to algorithmic bias in low-resource languages, and risks of "metacognitive laziness" emerged from over-reliance on automated tools.
Conclusion: The successful integration of AI in multilingual contexts depends on a shift from content generation to pedagogical scaffolding. Designers must prioritize "Human-in-the-loop" models that balance computational efficiency with human oversight to provide the emotional engagement and cultural authenticity that AI currently lacks.
Taraneh Yarahmadi, Zane Whitcomb· Review of Artificial Intelli...· 0 citations