Using mixed reality to facilitate education in robotics and AI
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it