CuriosityXR: Context-aware Education Experiences with Mixed Reality and Conversation AI
Bibliographic record
Abstract
The educational landscape is undergoing a fundamental shift towards a learner-centric model, emphasizing engagement, interaction, and personalization in the learning process. This study investigates new technologies that enable immersive, self-guided, and curiosity-driven educational experiences, addressing these crucial elements. The research delves into Mixed Reality (MR) as a tool for constructing a context-aware system that nurtures learners’ inquisitiveness while enhancing memory retention. The paper presents the design and development of "Curiosity XR," an MR headset application created using a research-through-design methodology, acting as a platform for educators to develop contextual and multi-modal interactive mini-lessons. Learners can engage with these lessons and also benefit from AI-supported learning content. The evaluation of this design involves a user participant study and subsequent interviews, revealing greater engagement levels, increased curiosity to learn, and improved visual content retention among participants. This work aims to encourage further exploration within the MR domain and promote the integration of MR and AI for the advancement of curiosity-driven education.
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.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".