Preparing Ai-Ready Information Science Graduates: Extending Foi-Aimil Through Reciprocal Experiential Benchmarking and The Ai/Ml Information Lifecycle

Abstract

Artificial intelligence (AI) is transforming the competencies required of Information Science graduates as information discovery, knowledge organisation, research support, analytics and professional decision-making become increasingly AI-mediated. This paper extends Festival of Ideas (FOI)-based experiential pedagogy with AI-Mediated Information Literacy (AIMIL) as the FOI-AIMIL framework for developing AI-ready Information Science graduates through experiential learning, authentic assessment and governance-oriented practice. Using an exploratory qualitative case-study and theory-extension design, the study is informed by a pseudonymised analysis of an official reciprocal academic visit by a Malaysian Public University (MPU) to a Chinese Applied Technology University (CATU) in southeastern China in May 2026. Particular attention is given to an industry-integrated School of Information Technology ecosystem involving AI-enabled applications, coding, intelligent systems, software development, practical experimentation and embedded industry expertise. Evidence is coded as Observed, Reported, Documented, AI-Assisted or Inferred and interpreted through the six AIMIL capabilities of Frame, Engage, Triangulate, Trace, Verify and Construct. A complementary crosswalk maps these capabilities onto the AI/ML information lifecycle from data and model through output, human interpretation, evidence and decision, while treating AIMIL as a recursive governance layer rather than a substitute for technical model-development methodologies. The analysis generates five theoretically relevant mechanisms for graduate formation: authentic AI exposure, situated industry learning, cognitive infrastructure, epistemic debugging through recursive verification, and evidence-to-professional-practice translation. The paper proposes the FOI-AIMIL Reciprocal Experiential Intelligence Cycle and an AI-Ready Information Science Graduate profile integrating information intelligence, AI-mediated judgement, provenance and verification, AI/digital analytics, governance and professional adaptability. Five consolidated propositions are advanced for future empirical testing. The study argues that graduate readiness should not be defined by AI tool proficiency alone, but by the capacity to interrogate AI-mediated information, establish provenance, evaluate evidence, govern information responsibly and defend professional decisions. The framework offers a transferable basis for curriculum redesign, authentic assessment, academic library education and industry-university collaboration in the AI era.