Knowledge Resource
Research Summary: Assessment of teacher performance and learner behavior using artificial intelligence in higher education: an integrative review of literature
- Original authors
- Attribution requires verification
- Original source
- Frontiers in Education
- Summary & Analysis prepared by
- Aziz Shuaib Ausi
- Resource type
- Research Summary / Knowledge Resource
- Resource published on AZIZ OS
- 29 October 2026
- Reading time
- 1 min
- Publication type
- Knowledge Resource
- Availability
- Open access
About this Summary & Analysis
AZIZ OS provides independently prepared summaries and analytical interpretations of externally published research and knowledge sources. The underlying works remain attributable to their original authors and rights holders. This resource is intended to improve accessibility and understanding and does not replace the original publication.
A systematic integrative review published in Frontiers in Education synthesizes findings from 21 rigorously appraised studies examining the application of artificial intelligence in higher education quality assurance. The analysis explores how adaptive, data-driven methodologies evaluate teacher performance and learner behavior, highlighting key technological trends and avenues to enhance instructional quality and student support systems.
Why it matters
This development signifies an institutional shift from retrospective, manual teaching evaluations toward real-time, automated pedagogical analytics. For higher education governance, adopting AI-enabled quality assurance creates new capabilities for proactive intervention in instructional quality and student outcomes, directly impacting institutional competitiveness and academic accreditation. Managing this shift requires balancing technological potential with methodological rigor and academic trust.
Key insights
- Artificial intelligence is driving a transition toward adaptive, data-driven assessment frameworks in higher education quality assurance.
- Out of 367 initially identified records, 21 studies met rigorous PRISMA selection and Mixed Methods Appraisal Tool (MMAT) quality criteria for synthesis.
- AI-driven assessment approaches are specifically being leveraged to analyze both educator performance and student learning behaviors.
- Integrative methodologies combining thematic analysis and keyword network mapping demonstrate emerging systemic opportunities to improve teaching effectiveness and targeted learner support.
Source
Frontiers in Education — https://www.frontiersin.org/articles/10.3389/feduc.2026.1949516
Related intelligence and resources
Previous
Assessing grammatical and lexical competence among Honduran lower-secondary students: preliminary validity evidence from the EGLCA-9
Next
Governing the hybrid writer: ethics-washing discourse and algorithmic governmentality in academic AI platforms
Rigor, commitment, and generative AI: a conceptual framework for student engagement in EAP writing
Knowledge Resource
Sustainable internationalisation of African higher education: African strategies, global lessons and epistemic justice
Knowledge Resource
Structural barriers to student career preparedness: institutional perspectives from a historically disadvantaged South African University
Knowledge Resource
Artificial intelligence applications in forensic science and their implications for education and training
Knowledge Resource
A model for enhancing the quality of geography education based on the integration of Artificial Intelligence and Geographic Information Systems
Knowledge Resource
Governing the hybrid writer: ethics-washing discourse and algorithmic governmentality in academic AI platforms
Knowledge Resource
Citation
Cite the original work (APA 7)
The original source is authoritative for this citation. Cite the source publication directly — this attribution is pending verification. Open the original source.
Verification
This is an authenticated AZIZ OS resource record.
- Verification ID
- ASA-EXE-2026-01486
- Version
- v1.0 · r0
- Issued
- 29 October 2026
- Resource prepared by
- Aziz Shuaib Ausi
- Resource status
- Research Summary / Knowledge Resource
- Underlying work
- Assessment of teacher performance and learner behavior using artificial intelligence in higher education: an integrative review of literature
- Original authors
- Attribution requires verification
- Original source
- Frontiers in Education
- Provenance status
- Attribution requires verification
- Rights
- Underlying publication rights remain with the respective copyright holder(s). Refer to the original source for authoritative publication and licensing information.
This verification confirms the AZIZ OS resource record and its documented provenance. It does not establish authorship of the underlying external work.