Conversational agents in virtual worlds: Bridging disciplines
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
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.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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