AI evidence extraction

Artificial Intelligence-Driven Innovation In Higher Education System In India

Shabana · Journal of Informatics Education and Research · 2026

AI-generated evidence extraction, verified across multiple analytical personas. Not a substitute for the peer-reviewed original.

8/10

Relevance

Evidence

Citations

0.00

FWCI

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.52783/jier.v6i1.4418

Methodology & findings

Study design

Systematic review of empirical studies published between 2019 and 2024, synthesizing current trends, applications, and outcomes of AI adoption in Indian higher education institutions using theoretical frameworks including the Technology Acceptance Model, Cognitive Load Theory, and Self-Determination Theory..

Primary method

Systematic review methodology with narrative synthesis; theoretical framework synthesis using Technology Acceptance Model, Cognitive Load Theory, and Self-Determination Theory.

Main result

The study found that "AI-enabled personalized learning systems, intelligent tutoring, learning analytics, and generative AI tools enhance learner engagement, instructional efficiency, and academic support, while AI-based administrative systems improve governance, resource optimization, and decision-making." Additionally, the paper notes that "AI's growing role in academic research through automation of literature review, data analysis, modelling, and research communication" contributes to research enhancement across Indian higher education institutions.

Reports effect sizes.

Research paradigm

Interpretivist/critical realist with systematic evidence synthesis

Author conclusions

The authors conclude that "AI can serve as a critical enabler for improving quality, equity, and global competitiveness of Indian higher education, provided it is implemented through a human-centric, ethical, and policy-aligned approach." This emphasizes conditional adoption dependent on adherence to ethical and policy frameworks.

Risk of bias

Publication bias (review limited to published studies 2019-2024); Geographic bias (focus on India-specific context may limit generalizability); Selection bias in study inclusion criteria not explicitly detailed; Potential funding bias from policy-aligned research interests; Selection bias in literature review scope (2019-2024 timeframe may exclude earlier foundational work); Potential geographic bias toward documented institutions with better reporting practices; Lack of explicit quality assessment criteria for included studies; Language bias (likely English-language studies predominate)

Limitations

The authors identify that "significant challenges persist, including infrastructural constraints, faculty readiness, ethical concerns, data privacy, and risks to academic integrity." The paper acknowledges contextual limitations specific to India's educational landscape without providing quantitative bounds on review scope or specific PRISMA compliance details.

Open questions raised

Limited empirical evidence on long-term effectiveness of AI interventions in Indian context
Need for research on faculty development and readiness for AI integration
Gaps in understanding equity and access implications of AI adoption across diverse institutional types
Insufficient evidence on data privacy frameworks and ethical governance of AI in Indian higher education
Need for studies on student outcomes and learning equity in AI-enhanced environments
Infrastructure development in underserved Indian institutions

Data: not_statedCode: not_statedExtracted from: pdf

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