AI evidence extraction

Over-Automation in Higher Education

Narendra Rathnaraj, Firdaus Bashir · Advances in computational intelligence and robotics book series · 2026

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

6/10

Relevance

1/4

Quality (LMQS)

Evidence

Citations

0.00

FWCI

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.4018/979-8-3373-9484-8.ch014

Methodology & findings

Study design

Conceptual analysis and ethical taxonomy development; hermeneutic/interpretive review of over-automation risks in educational AI systems

Main result

The chapter identifies that "the most consequential harms emerge from the institutionalisation of AI reliance that diffuses responsibility, reduces learner agency, and embeds procedural injustice." The study maps "six recurring risk clusters, including accountability, responsibility, pedagogical complacency, procedural injustice, metacognition, and erosion of learner agency" in over-automated education systems.

Reports effect sizes.

Research paradigm

Critical/interpretive

Author conclusions

The authors conclude that the chapter "concludes with actionable design and policy recommendations for responsible AI use in education that protects learner agency, trust, and critical thinking while retaining efficiency gains."

Risk of bias

Author's implicit value position favoring learner agency and critical thinking may bias framing of automation risks; Selection of risk clusters may reflect author preferences rather than exhaustive empirical identification; No empirical data presented to validate the taxonomy or prevalence of identified risks; Potential confirmation bias in identifying and illustrating risk clusters

Open questions raised

The abstract does not explicitly identify specific future research gaps or directions beyond recommending responsible AI design and policy approaches.
The paper identifies the need for frameworks addressing over-automation risks in education and calls for design and policy interventions to balance AI benefits with protection of learner agency and critical thinking.
The paper identifies gaps related to the need for protective measures against over-automation, including safeguards against cognitive offloading, weakening of critical thinking, and erosion of learner agency in institutionalized AI-reliant educational systems.
The paper implicitly identifies gaps in responsible AI governance in educational institutions and the need for human-centered ethical frameworks for AI deployment in learning systems.
The abstract does not explicitly identify future research directions, but implicitly suggests gaps in: empirical validation of the taxonomy, institutional implementation studies, longitudinal effects on learner cognition, and comparative analysis of safeguard effectiveness across educational contexts.
The chapter addresses gaps in ethical frameworks for AI use in education by developing a Human-Centred Ethical Risk Taxonomy and identifying specific institutional and pedagogical risks from over-automation.

Data: not_statedCode: not_statedExtracted from: pdfAgreement 76%

Explore related topics

Responsible AI Use AI Governance Conceptual