Conversational agents in virtual worlds: Bridging disciplines
Bibliographic record
Abstract
Abstract This paper examines the effective deployment of conversational agents in virtual worlds from the perspective of researchers/practitioners in cognitive psychology, computing science, learning technologies and engineering. From a cognitive perspective, the major challenge lies in the coordination and management of the various channels of information associated with conversation/communication and integrating this information with the virtual space of the environment and the belief space of the user. From computing science, the requirements include conversational competency, use of nonverbal cues, animation consistent with affective states, believability, domain competency and user adaptability. From a learning technologies perspective, the challenge is to maximise the considerable affordances provided by conversational avatars in virtual worlds balanced against ecologically valid investigations regarding utility. Finally, the engineering perspective focuses on the technical competency required to implement effective and functional agents, and the associated costs to enable student access. Taken together, the four perspectives draw attention to the quality of the agent–user interaction, how theory, practice and research are closely intertwined, and the multidisciplinary nature of this area with opportunities for cross fertilisation and collaboration.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".