Supporting students and educators in using generative artificial intelligence
Bibliographic record
Abstract
The use of generative artificial intelligence (genAI) in university settings is a current topic of debate, with a range of viewpoints regarding the extent to which these tools should be used by students (Ahmad et al., 2023) and the potential applications of genAI tools in higher education (Yu & Guo, 2023). Concerns have also been raised regarding the potential student misuse of genAI tools, and the ability of these tools to score a passing grade in some university subjects (Nikolic et al., 2023). RMIT University’s position is that we must build the capability in our students to engage with AI as part of the current and future requirements of work. The RMIT units responsible for academic quality and for education innovation have created a set of statements that educators can choose from when designing assessment tasks. These statements include there being no restrictions on the use of genAI tools in the assessment task, that genAI tools can be used with limitations, or that genAI tools cannot be used. If students are permitted to use genAI tools in assessment tasks, they must appropriately acknowledge and reference the use of these tools and their outputs. In the library, we were tasked with creating citing and referencing guidelines for AI-generated content for each of the styles used at our institution, including APA 7th, IEEE, Chicago 17th and AGLC4. A challenge of this project was that there was either no specific genAI referencing advice provided by the style manual editors, or the advice was limited to a specific tool, e.g. ChatGPT in the case of APA 7th (McAdoo, 2023) and Chicago 17th (The Chicago Manual of Style Online, n.d.). We adapted the existing style advice for referencing software for the APA 7th, Harvard, Chicago 17th, and IEEE styles, the advice for referencing internet sources for Vancouver, and the advice for referencing personal correspondence for AGLC 4. We created referencing guidelines for both AI-generated text and images, as well as when genAI was used for background research. We also incorporated current Australian copyright advice into these guidelines, in which authorship can only be granted to human creators, and so the creator of the tool was used as the author rather than the tool itself. These guidelines are housed in a subject guide (RMIT, 2023) which has received more than 17,000 views between February and July 2023. We also updated our Academic Integrity Awareness (AIA) microcredential to include educative information about genAI tools. We included guidance relating to the inaccurate information and ethical concerns in some of the current tools, as well as placing these tools within the overall context of academic integrity. This microcredential is used as a component of assessment tasks in many disciplines across our institution. These resources assist students in maintaining academic integrity when using genAI tools in their learning, and when using genAI in their future careers, as they reinforce the central requirement that the work of others (including work that is AI-generated) is appropriately acknowledged. These resources will continue to be updated as genAI tools evolve and become more widely used within learning.
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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 | 0.026 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.020 | 0.012 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".