Eight-week MI module sequence with assignments and compact assessment rubric¶
Design · All designs
Evidence · no claims cited
Description¶
The paper provides reusable curriculum artifacts: an eight-week module sequence aligned to the materials discovery workflow, three assignments (descriptor benchmarking, leakage and dataset-shift diagnosis, uncertainty-aware active learning), and a compact 0-2 scale rubric. The rubric translates 'workflow-aligned AI literacy for materials discovery' into observable performance indicators across seven competencies from data provenance to ethics and equity awareness.
Design Implications¶
Context¶
Requirements¶
- Deployable within existing materials science curricula as an upper-division elective module, graduate mini-course, workshop, bootcamp sequence, or cross-institution short course
Constraints¶
- Adaptable to local constraints such as credit hours, computing access, student preparation, and available datasets; assignments use public datasets such as Materials Project-derived datasets or OQMD subsets
Target Learners¶
- upper-division undergraduates
- graduate students
- workshop and bootcamp participants
Learning Goals¶
- documenting data provenance
- justifying representations
- evaluating model validity
- interpreting uncertainty
- connecting AI outputs to scientific judgment
Claims¶
Related Designs¶
- Pair-based, discovery-oriented LLM analysis activity following an eight-week disciplinary module
- Short online cross-disciplinary mini-course of themed modeling assignments for building systems thinking
- SPICE curriculum formative assessments: Rules Task, Debugging Task, and Engineering Task with cross-domain rubrics
Examples¶
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Key Sources¶
- Dongming Mei, Katherine Moore, and Ben Sayler. (2026). Preparing Students for AI-Powered Materials Discovery: A Workflow-Aligned Framework for AI Literacy, Equity, and Scientific Judgment. https://arxiv.org/abs/2605.09624
Page metadata
| Field | Value |
|---|---|
type |
design |
id |
eight-week-mi-module-sequence-rubric |
title |
Eight-week MI module sequence with assignments and compact assessment rubric |
description |
The paper provides reusable curriculum artifacts: an eight-week module sequence aligned to the materials discovery workflow, three assignments (descriptor benchmarking, leakage and dataset-shift diagnosis, uncertainty... |
status |
review |
generated |
by process:settle-candidates on 2026-10-10 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — dongming-mei-2026 |
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