Researchers Reveal Generative AI Maturity-Expectation Gap
A research team led by Professor Kim Do-hyung of Kookmin University published a study in Technovation proposing the Maturity-Expectation Gap (MEG) framework to measure differences between generative AI system readiness and stakeholder expectations. Using evaluator surveys paired with machine-learning analysis of academic research, the authors found expectation–maturity divergences reduce willingness to rely on AI, and noted structured-data evaluations appear more adoptable than qualitative tasks.
Key Points
- 1Introduces MEG framework using evaluator surveys and machine-learning analysis to quantify AI maturity-expectation gaps
- 2Shows divergent views across evaluators, policymakers and researchers, eroding confidence when expectations exceed maturity
- 3Indicates practitioners can adopt generative AI for structured-data evaluation sooner than qualitative judgment tasks
Scoring Rationale
Peer-reviewed framework and actionable findings justify high impact, but limited empirical breadth across domains constrains generalizability.
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