Beyond conventional teaching towards networked learning
Bibliographic record
Abstract
With Generative Artificial Intelligence (GenAI) adoption growing, education has seen the emergence of innovative technologies like chatbots. However, little research has examined the impacts of GenAI integration in specialized higher education contexts. This study explored graduate students’ experiences using a GenAI chatbot, PEARL, within a graduate-level teacher education course focused on teaching students how to collaboratively conduct program evaluations using practice cases. Four students participated in our study and shared perceptions of interviewing personas with PEARL when evaluating the practice cases. Thematic analysis identified advantages like enhanced efficiency and accessibility, plus limitations regarding authenticity of artificial interactions. Findings emphasized the continued importance of human guidance and peer learning to enrich GenAI-enabled education aligning with principles of networked learning. Students highlighted the need for ethical considerations despite interacting with artificial entities, underscoring nuanced understanding. The significance of collaborative analysis and ongoing iterative improvements also emerged as themes integral to meaningful learning. Although GenAI presents transformational potential in instructional designs, findings support the use of blended approaches that strategically integrate its advantages with human activity and collaborative inquiry. The study makes contributions by elucidating domain-specific nuances of integrating GenAI into teaching in higher education. Practical implications encourage scaffolding GenAI curricula to promote authenticity and collaborative knowledge construction. Further research could examine variations across disciplines, technologies, and demographics. Overall, as GenAI shapes academia’s evolution, reflective pedagogical examination will be key to evidence-guided integration. This exploratory study presents a preliminary yet important step, unveiling opportunities for networked learning and complexities of GenAI adoption in teaching program evaluation skills in education contexts.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".