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| Format: | Recurso digital |
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Zenodo
2026
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| Accès en ligne: | https://doi.org/10.5281/zenodo.20319476 |
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| _version_ | 1866902038281977856 |
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| author | Bakshi, Ruchir |
| author_facet | Bakshi, Ruchir |
| contents | AAFL is an instructional systems design framework for the agent era — where AI agents author first drafts and humans serve as Human-in-the-Loop judgment-holders, anchored in workplace performance as the organizing outcome. The framework keeps ADDIE's five-phase spine and adds eight HITL decision gates, three cross-cutting layers (Governance, Evaluation-as-Spec, Orchestration), the Translator's Loop, a Proportional Restraint Scale (PRS-1 through PRS-4) with classification-aware maturity caps, and a six-dimension eval framework. |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_20319476 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | AAFL: An Agent-Augmented Framework for Learning Bakshi, Ruchir instructional design instructional systems design ADDIE AI agents human-in-the-loop workplace performance human performance technology federal training agent-augmented learning AAFL is an instructional systems design framework for the agent era — where AI agents author first drafts and humans serve as Human-in-the-Loop judgment-holders, anchored in workplace performance as the organizing outcome. The framework keeps ADDIE's five-phase spine and adds eight HITL decision gates, three cross-cutting layers (Governance, Evaluation-as-Spec, Orchestration), the Translator's Loop, a Proportional Restraint Scale (PRS-1 through PRS-4) with classification-aware maturity caps, and a six-dimension eval framework. |
| title | AAFL: An Agent-Augmented Framework for Learning |
| topic | instructional design instructional systems design ADDIE AI agents human-in-the-loop workplace performance human performance technology federal training agent-augmented learning |
| url | https://doi.org/10.5281/zenodo.20319476 |