Kamarulzaman, Wirawani and Kartiwi, Mira and Megat Ramli, Puteri Azlian (2026) Human factors in AI-assisted accreditation: exploring trust, cognitive impact, and usability. Scientific Culture, 12 (4). pp. 11659-11673. ISSN 2408-0071 E-ISSN 2407-9529
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Abstract
As higher education accreditation grows more data-intensive and complex, Artificial Intelligence (AI) is increasingly viewed as a means to improve efficiency and consistency; however, successful implementation depends on human factors such as trust, cognitive impact, and usability. This study examines evaluators’ perceptions of AI-assisted accreditation (Research Question 1) and investigates how trust, cognitive load, usability, perceived usefulness, and ethical use relate to acceptance and readiness for adoption (Research Questions 2–3) within Malaysian accreditation contexts. Using a cross-sectional, survey-based mixed-methods design, data were collected from 80 accreditation stakeholders, including Malaysian Qualifications Agency (MQA) panel assessors and higher education institution quality assurance administrators. Closed-ended survey items (five-point Likert scale) provided quantitative evidence for Research Questions 1–3, while open-ended questions were analysed thematically to contextualise and explain the quantitative findings for Research Question 1. Results show strong overall support for AI as a supportive (not substitutive) tool, particularly for repetitive and structured tasks such as report summarisation, mapping learning outcomes to the Malaysian Qualifications Framework, and identifying gaps against MQA standards (COPPA/COPIA). Trust in AI was generally positive but conditional on transparency, verifiability against human judgement, demonstrated reliability across cases, and the availability of training and guidelines; concerns centred on unreliable outputs (including hallucinations), over-reliance, and data confidentiality. Correlational analyses showed significant positive relationships among perceived usefulness, trust, usability, and ethical use. The findings highlight the need for human-centred, explainable, and context-specific AI designs, alongside governance and capacity-building initiatives, to enable responsible and effective AI integration in accreditation processes.
| Item Type: | Article (Journal) |
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| Uncontrolled Keywords: | Artificial Intelligence; accreditation; higher education quality assurance; trust in automation; cognitive load; usability; MQA |
| Subjects: | A General Works > AI Indexes (General) L Education > L Education (General) L Education > LB Theory and practice of education L Education > LB Theory and practice of education > LB2300 Higher Education T Technology > T Technology (General) T Technology > T Technology (General) > T173.2 Technological change |
| Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): | Kulliyyah of Education > Department of Educational Psychology & Counseling Kulliyyah of Education Kulliyyah of Information and Communication Technology Kulliyyah of Information and Communication Technology |
| Depositing User: | Dr Wirawani Kamarulzaman |
| Date Deposited: | 10 Jul 2026 10:56 |
| Last Update: | 10 Jul 2026 10:56 |
| Queue Number: | 2026-06-Q3850 |
| URI: | http://irep.iium.edu.my/id/eprint/129627 |
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