Aug 2026· Frontiers in Computer Science and Artificial Intelligence· Vol 5, pp. 246-259· 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
The rapid advancement of Artificial Intelligence (AI) is transforming higher education by reshaping conventional teaching practices, learning processes, and mechanism of student support. This study examines the role of AI-driven technologies in improving teaching methodologies, student engagement, and learning outcomes in higher education, with particular emphasis on AI-enhanced feedback systems, educational chatbots, predictive analytics, and AI-assisted learning and examination preparation. The study adopts mixed-methods research framework integrating quantitative and qualitative approaches to develop a comprehensive understanding of the educational applications and implications of AI. Quantitative evidence is proposed to be obtained through structured surveys examining AI usage, perceived learning outcomes, and levels of student engagement, while qualitative insights are derived from interviews, focus group discussions, and case studies involving relevant educational stakeholders. The study further examines the ethical and institutional challenges associated with AI adoption, particularly data privacy, algorithmic bias, transparency, and unequal access to digital technologies. The evidence discussed in the study indicates that AI-enabled educational interventions can facilitate personalised learning provide timely and context-specific feedback, strengthen student engagement, support the identification of academically at-risk students, and improve learning and examination outcomes. At the same time, the responsible integration of AI requires appropriate institutional safeguards to ensure fairness, privacy, transparency, and inclusivity. The study concludes that 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
This study investigates the integration of artificial intelligence (AI) technologies in higher education institutions in Kazakhstan. It aims to explore students' and academics' perceptions, usage patterns, challenges, and training needs. The findings show that AI is increasingly seen as a transformative academic tool, especially for research, language learning, writing support and problem‐solving. Although many participants first encountered AI through media exposure or academic requirements, their engagement has shifted from initial scepticism to routine use, with many reporting daily reliance on AI tools. Participants highlighted AI's potential to create more personalised and interactive learning environments, particularly through applications like chatbots and adaptive learning systems. However, concerns were raised about over‐dependence on AI, which could weaken critical thinking and problem‐solving skills. These concerns align with previous research warning of passive learning behaviours in AI‐supported educational settings. Despite widespread AI adoption, the study identifies significant gaps in institutional infrastructure and the absence of systematic training programmes. Most participants developed their AI skills through informal, self‐guided learning rather than formal university instruction, underscoring the urgent need for structured AI literacy initiatives within higher education. Ethical issues, including algorithmic bias and data privacy, were also highlighted, emphasising the importance of responsible AI use and ethical governance in institutional policies and curricula. The results suggest that universities should invest in AI infrastructure, implement comprehensive AI literacy programmes and incorporate AI ethics education into academic curricula to prepare both students and faculty for effective and responsible AI use. This study contributes to the growing body of literature on AI adoption in higher education by providing empirical insights from the Kazakhstani context.
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
The increasing use of Generative Artificial Intelligence in Higher Education has created new challenges for academic integrity, intellectual authorship, and the development of critical thinking. This study analyzes these challenges and evaluates a pedagogical intervention designed to promote the responsible use of Generative Artificial Intelligence in graduate education. The study was conducted between February 2025 and June 2026 and involved a total of 50 graduate students from three cohorts enrolled in two Master’s programs in Engineering and Business Administration. A qualitative educational action research approach was adopted, based on the analysis of academic assignments, similarity reports, and Artificial Intelligence-assisted writing detection using Turnitin, together with classroom observations and reflective discussions. The pedagogical intervention incorporated strategies based on the UNESCO (2023) guidance, structured prompt design, and the Socratic model. The findings revealed frequent use of Generative Artificial Intelligence-generated content without adequately paraphrasing the generated material, verifying information, or consulting the scientific literature. Following the intervention, students demonstrated greater attention to question formulation, information validation, and the use of reliable academic sources. The study contributes empirical evidence 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· 0 citations
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