Aug 2026· Frontiers in Computer Science and Artificial Intelligence· 0 citations
TL;DR
This document outlines the conceptual, theoretical, and methodological underpinning of the AI-Augmented Pedagogy Integration Model (AAPIM), which has now been further supported by a growing evidence base of 2025–2026 meta-analyses and systematic reviews.
Abstract
The transformative opportunities and challenges of generative AI tools, especially large language models (LLMs) such as ChatGPT, have emerged rapidly in higher education. The adoption of generative AI in undergraduate education has increased dramatically in 2024–25, whereas institutions' pedagogical approaches, institutional policies, and evidence of learning outcomes are less developed than their use. Previous research has primarily focused on the short-term acceptance of the tools or single academic integrity issues without considering them in relation to each other. Recent systematic reviews and meta-analyses (2025–2026) have begun to measure the impact of GenAI on learning outcomes and trace authorship and integrity concerns in greater detail. However, few studies have systematically investigated the impact of GenAI on learning outcomes, pedagogical design, and academic integrity across a variety of learner populations. This study will explore (1) the observable effects of AI-supported personalised learning on student learning outcomes at the undergraduate level; (2) faculty and student attitudes towards the impact of AI on pedagogical redesign; and (3) institutional policies to ensure academic integrity while supporting and facilitating AI-supported learning. The proposed convergent mixed methods design will involve validation of the survey instrument (n = 420 undergraduate and faculty students across three institutions) and a quasi-experimental pre-post assessment study. Structural equation modelling (SEM) and thematic coding of qualitative data were used in the planned analysis. This study aims to produce a theoretically sound, testable framework that will benefit evidence-based strategies for the adoption of AI in higher education, the AI-Augmented Pedagogy Integration Model (AAPIM). This document outlines the conceptual, theoretical, and methodological underpinning, which has now been further supported by a growing evidence base of 2025–2026 meta-analyses and systematic reviews, with empirical data reported when the data collection is complete
AI has considerable potential to complement conventional pedagogical practices and contribute to more adaptive and student-centered higher education, provided that its implementation is guided by sound pedagogical principles and responsible governance frameworks.
Sugandha Sahay, Gouranga Patra· International Journal for Sc...· 0 citations
Artificial intelligence has moved rapidly from specialised analytics and tutoring applications to widely accessible generative systems capable of producing text, code, images, explanations, and feedback. In higher education, this shift has created a closely coupled set of pedagogical opportunities and integrity risks. This critical narrative review examines how artificial intelligence is reshaping student learning, assessment, and academic integrity, with emphasis on the conditions under which educational value is strengthened or weakened. Literature published from 1 January 2019 to 30 May 2026 was identified through accessible scholarly indexes, citation searching, authoritative institutional sources, and verification against DOI and journal records. The evidence indicates that artificial intelligence can expand access to explanation, formative feedback, language support, ideation, and practice, and controlled studies increasingly report benefits for selected learning outcomes. Yet effects are heterogeneous, often short term, and highly sensitive to task design, student expertise, prompting skill, feedback literacy, and the degree of human oversight. Gains in efficiency or performance do not necessarily demonstrate durable understanding, metacognition, or independent capability. Assessment is therefore the pivotal institutional problem: generative systems can assist feedback and evaluation while simultaneously weakening the validity of unsupervised products as evidence of individual achievement. Automated detection is not a dependable solution because accuracy varies by detector, text type, language background, and model evolution, creating risks of false accusation and unequal treatment. The most defensible response is not unrestricted adoption or blanket prohibition, but an aligned model combining explicit AI literacy, process-rich and dialogic assessment, proportionate disclosure rules, human judgement, data governance, and fair procedures for investigating suspected misuse. The review concludes that artificial intelligence should be treated as a socio-technical component of curriculum and assessment rather than a stand-alone productivity tool. Its educational legitimacy depends on whether institutions can preserve epistemic agency, valid judgement of learning, equitable access, and accountable human responsibility.
A. Talib· Asian Journal of Education a...· 0 citations
The findings show that AI is increasingly seen as a transformative academic tool, especially for research, language learning, writing support and problem‐solving, and despite widespread AI adoption, the study identifies significant gaps in institutional infrastructure and the absence of systematic training programmes.
M. Doğan, B. Kashkhynbay, Zhaniyat Baltabayeva· European Journal of Educatio...· 0 citations
Generative Artificial Intelligence (GenAI) has rapidly transformed higher education practices, creating new opportunities for pedagogical innovation while introducing complex challenges related to assessment validity, academic integrity, and institutional governance. However, existing studies remain fragmented across technological adoption, learning processes, assessment practices, and ethical considerations, limiting a comprehensive understanding of how GenAI can be integrated responsibly into higher education ecosystems. This systematic literature review aims to synthesize current evidence on the educational implications of GenAI by examining its influence on teaching transformation, student learning, assessment redesign, and academic integrity governance. Following the PRISMA framework, relevant studies were systematically identified, screened, and analyzed to reveal emerging patterns, challenges, and future research directions in GenAI adoption within higher education. The synthesis revealed four interconnected themes: (1) transformation of teaching practices through AI-supported instructional design and efficiency improvement, (2) enhancement of student learning through personalization and self-regulated learning support, (3) evolution of assessment toward authentic and competency-oriented approaches, and (4) development of institutional governance frameworks addressing ethical, privacy, transparency, and integrity concerns. The findings indicate that successful GenAI integration requires a balanced approach combining technological capability, pedagogical redesign, and responsible governance. This review contributes to Artificial Intelligence in Education (AIED) research by proposing an integrated perspective for sustainable GenAI adoption and identifying priorities for future empirical investigations.
Syusinka Rahmatika, Martanto, Ryan Hamonangan· Immortalis Journal of Interd...· 0 citations
Empirical evidence is contributed from educational action research showing that structured pedagogical interventions can promote the critical, ethical, and responsible use of Generative Artificial Intelligence in education.
Rodrigo Florencio da Silva· Information· 1 citation
Across the reviewed studies, generative AI was found to enhance language learning through personalized feedback, increased learner autonomy, and greater learning engagement, but concerns regarding academic integrity, AI literacy, ethical issues, and institutional readiness remain significant challenges to its sustainable implementation.
N. H. Hong Nhung· International journal of soc...· 0 citations