AI Integration Methodology: Does One Size Fit All?
Bibliographic record
Abstract
The rapid evolution of Generative AI is reshaping the Information Systems (IS) landscape, transforming how organizations retrieve knowledge, support decision-making, and co-create value. Yet for many educators and practitioners, a clear, actionable methodology for integrating this powerful technology into enterprise or academic environments remains elusive. This Professional Development Symposium introduces and discusses a structured, human-centered approach to AI deployment that emphasizes mutual trust and coordinated action in complex, dynamic contexts. This session will appeal to a broad AMCIS audience, including researchers, educators, and practitioners engaged in digital transformation, IS curriculum development, AI ethics, and socio-technical systems. It is particularly relevant to multiple SIGs, such as SIGOSRA, SIGED, SIGAI, and SIGGreen, and seeks to foster interdisciplinary dialogue on how best to prepare both learners and organizations for the AI era. Through a combination of conceptual framing, real-world case studies, and interactive activities, participants will explore the flexibility and relevance of this methodology across diverse educational and organizational settings. By encouraging an open exchange of experiences and perspectives, this PDS aims to deliver immediate pedagogical value and cultivate long-term collaboration. The session culminates in a critical conversation on the provocative question: Does one size fit all in AI integration?
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 | 0.166 | 0.206 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.008 | 0.042 |
| Scholarly communication | 0.018 | 0.046 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".