Report: Grand Challenges in IDT - Coordinating Research and Development on Significant Problems of Practice
Bibliographic record
Abstract
This white paper reports on the OTESSA Santa Fe Colloquium, a two-day scholarly convening held in June 2025 as part of the OTESSA 2025 Congress. Designed as a pilot for a collaborative, problem-centered model of inquiry, the colloquium brought together researchers, instructional designers, faculty, and journal editors to shift attention from educational technologies toward significant problems of practice in instructional design and technology (IDT). Through structured activities, including a World Café dialogue model and a Grand Challenges workshop, participants collectively identified, refined, and prioritized pressing challenges facing the field. Three priority grand challenges emerged: considering the whole human in education, supporting learner mental wellness, and redefining the purposes and structures of education. This paper synthesizes key insights from conference activities and post-colloquium reflection papers, highlighting shared themes, areas of alignment, and opportunities for coordination. Rather than presenting empirical findings, the report offers a practice-oriented synthesis intended to inform strategic planning, future convenings, and coordinated research and practice efforts within OTESSA and the broader IDT community.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.062 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.019 | 0.011 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".