From Procedural Dependency to Scholarly Autonomy: Evaluating the Impact of the FOI–AIMIL Authentic Assessment Framework on Postgraduate Information Science Students

Contemporary postgraduate education faces growing pressure to cultivate transferable, higher-order competencies that transcend rote knowledge acquisition. This qualitative case study examines the pedagogical impact of the Festival of Ideas–AI-Mediated Information Literacy (FOI–AIMIL) authentic assessment framework on postgraduate students enrolled in IML630 (Library Analytics and Metrics) and IMC701 (Seminar on Information Science) at Universiti Teknologi MARA (UiTM). The FOI–AIMIL framework integrates field-based participation in a national Festival of Ideas with AI-mediated information literacy scaffolding and continuous digital mentoring delivered through WhatsApp. Employing a naturalistic qualitative dataset of WhatsApp communications spanning the pre-, during-, and post-assessment phases, the study applies Braun and Clarke's reflexive thematic analysis to trace cognitive, behavioural, and affective learning transformations. The theoretical framework draws on Kolb's Experiential Learning Theory, Bloom's Revised Taxonomy, and Constructivist Pedagogy to interpret the findings. Four overarching themes emerged from the analysis: (1) From Logistical Anxiety to Reflective Inquiry; (2) AI-Mediated Scaffolding and Critical Engagement; (3) Collaborative Knowledge Construction in Digital Spaces; and (4) Emergent Scholarly Identity and Metacognitive Awareness. The analysis reveals a discernible and progressive shift from procedural dependency and logistical anxiety to demonstrable scholarly autonomy, methodological confidence, and reflective practice. The study contributes to the authentic assessment literature by demonstrating how AI-mediated information literacy instruction, when embedded within a structured experiential framework, can catalyse higher-order learning among postgraduate information science students. While situated within a single Malaysian public university, the findings provide an analytically transferable model for authentic AI-mediated assessment that may inform comparable postgraduate learning contexts. Findings carry significant implications for curriculum designers, academic developers, and policymakers in higher education seeking to build AI-augmented, authentically designed pedagogical environments.

authentic assessment, AI-mediated information literacy, FOI–AIMIL, experiential learning, postgraduate education, WhatsApp mentoring, thematic analysis

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