Using mixed reality to facilitate education in robotics and AI
Bibliographic record
Abstract
Using robots as part of any curriculum requires careful man-agement of the significant complexity that physical embod-iment introduces. Students need to be made aware of this complexity without being overwhelmed by it, and navigat-ing students through this complexity is the biggest challenge faced by an instructor. Achieving this requires a framework that allows complexity to be introduced in stages, as students’ abilities improve. Such a framework should also be flexible enough to provide a range of application environments that can grow with student sophistication, and be able to quickly change between applications. It should be portable and main-tainable, and require a minimum of overhead to manage in a classroom. Finally, the framework should provide repeata-bility and control for evaluating the students ’ work, as well as for performing research. In this paper, we discuss the ad-vantages of a mixed reality approach to applying robotics to education in order to accomplish these challenges. We intro-duce a framework for managing mixed reality in the class-room, and discuss our experiences with using this framework for teaching robotics and AI.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".