Which Decisions Remain the Educator's Own: The Hybrid Decision-Making Model for AI-Integrated Education
- AIOR Admin
- 4 days ago
- 1 min read
Pauline P. L. Chin
Meragang Sixth Form College
When educators working with artificial intelligence (AI) are asked which decisions remain their own to make, most know the boundary exists. Few have a structure for it. This paper addresses that absence directly by proposing the Hybrid Decision-Making Model, a three-tier framework that assigns decision authority across AI-integrated higher education practice. Tier 1 covers routine, reversible decisions where AI leads and the human retains oversight. Tier 2 covers decisions requiring both AI-provided data and human contextual judgement, where neither contribution alone produces a valid outcome. Tier 3 covers decisions whose irreversible consequences, ethical weight, and relational specificity make human authority a structural requirement rather than a preference. The HDM is grounded in three theoretical foundations: Simon's (1947, 1990) account of bounded rationality, which explains why the absence of decision authority produces predictable drift towards AI-generated outputs; Engelbart's (1962) and Baker's (2016) principle of augmentation, which establishes that AI should extend human capability rather than replace it; and the hybrid intelligence and governance frameworks of Holstein et al. (2019, 2020), Akata et al. (2020), and IMDA and PDPC (2020), which provide the dimensions and human involvement taxonomy from which the three tiers are derived. Empirical support is drawn from a qualitative study of seven higher education educators who consistently drew the same structural distinctions the HDM formalises, in the absence of a formally named decision authority structure for AI-integrated practice. The model does not prescribe institutional behaviour. It names the conditions under which decision authority is maintained. The structure was already there. This paper simply gives it a name.
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