A Multimodal Interactive Framework for Science Assessment in the Era of Generative Artificial Intelligence
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 The rapid evolution of generative artificial intelligence (GenAI) is transforming science education by facilitating innovative pedagogical paradigms while raising substantial concerns about scholarly integrity. One particularly pressing issue is the growing risk of student use of GenAI tools to outsource assessment tasks, potentially compromising authentic learning and evaluations. Addressing these challenges requires reflection on existing assessment practices and features. This position paper advances a conceptual framework for science assessment through the lens of multimodality and interactivity . Multimodality emphasizes the use of diverse, organized semiotic resources for meaning making, while interactivity characterizes assessment environments where outcomes are shaped by students' actions. With the two dimensions, our multimodal interactive framework classifies assessments into four categories, with varying degrees of modality and interactivity. We argue that tasks with higher modality and interactivity can potentially overcome the concerns of GenAI on academic integrity. To further articulate this point, we provide concrete assessment examples for each category and explain how the prompt and response affordances in each assessment category help gauge students' understandings of key science constructs and identify tasks that are resistant or susceptible to AI‐based outsourcing. We conclude by discussing how the framework serves as a meaningful analytical tool for educational researchers and practitioners.
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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 | 0.037 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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