Assessing for Integrity in the Age of AI
Bibliographic record
Abstract
In this webinar, Dr. Sarah Elaine Eaton, explores the potential benefits and drawbacks of using AI in educational assessment. Although AI offers opportunities for efficiency and personalization, ethical considerations, including potential biases, privacy concerns and the risk of undermining academic integrity, need to be addressed. AI can enhance assessment practices by automating grading and feedback, enabling frequent assessments and providing personalized learning paths. However, AI algorithms can perpetuate biases, struggle to evaluate nuanced responses and raise privacy concerns about student data. Maintaining academic integrity in a technology-driven classroom is crucial, particularly avoiding unreliable and potentially biased AI-text detection tools. To ensure equity, diversity, inclusion, and accessibility in AI-powered assessments, it is important to incorporate accessibility and inclusion features for students with disabilities and use diverse and representative training data to minimize bias. This approach aligns with the principles of fairness and equity in AI assessment highlighted in the abstract, promoting a more inclusive learning environment. Ensuring fair and equitable AI-powered assessments requires diverse training data, regular audits for bias and transparency in assessment criteria. Strategies for ethical AI implementation include clear communication with students, data privacy protection, human oversight and ongoing system improvement. Keywords: artificial intelligence, GenAI, education, higher education, assessment, academic integrity, ethics, bias, equity, ed tech, disability, neurodiversity, inclusion, inclusive education How to cite this work: Eaton, S. E. (2024, December 4). Assessing for Integrity in the Age of AI [Online]. DOCEO AI. Calgary, Canada.
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 | 0.076 | 0.180 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.009 | 0.018 |
| Scholarly communication | 0.014 | 0.022 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".