Introduction to the Minitrack on Design and Appropriation of Knowledge and AI Systems
Bibliographic record
Abstract
The objective of this minitrack is to contribute to the body of knowledge that helps scholars and practitioners increase their collective understanding of (1) how knowledge and artificial intelligence (AI) systems are planned, designed, built, implemented, used, evaluated, supported, upgraded, and evolved; (2) how knowledge and AI systems impact the context in which they are embedded; and (3) the human behaviors reflected within and induced through both ( 1) and (2).By knowledge and AI systems, we mean systems in which human participants and/or machines perform work (processes and activities) related to the creation, retention, transfer and/or application of knowledge using information, technology, and other resources to produce informational products and/or services for internal or external customers (adapted from Alter 2008).Such systems may include, but are not limited to, knowledge management systems, decision systems, social media, expert systems, machine learning systems, and other AI systems as well as any other ITenabled knowledge processes.It is the eleventh year of the minitrack.We received six papers this year and after a rigorous review process, we accepted three for publication in the proceedings and online presentation at the conference.The first paper is titled Knowledge Transfer between Humans and Conversational Agents: A Review, Organizing Framework, and Future Directions.Authored by Prakash Chandra Sukhwal, Wei Cui, and Atreyi Kankanhalli, this paper presents a systematic literature review of empirical information system and human-computer interaction studies on the knowledge transfer between humans and conversational agents.After analyzing gathered papers, the authors provide a summary of the current state of empirical research, propose an organizing framework for synthesizing the prior research, identify gaps in understanding, and lay out future research directions on this topic.
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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.027 | 0.009 |
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".