Engaging students to become ‘practice ready’ and ‘digitally proficient’ taxation graduates
Bibliographic record
Abstract
Rationale Generative artificial intelligence (AI) is a topical issue. The importance of users being able to recognise the quality of outputs is crucial. Bearman et al. (2024) state that ‘university graduates should be able to effectively deploy the disciplinary knowledge gained within their degrees to distinguish trustworthy insights from the ‘hallucinatory’.’ Ballentine et. al (2024) suggest that generative AI provides an opportunity to move away from assessment that relies on rote learning to the use of critical assessment including authentic scenario-based examples. Such authentic assessment is regarded as an important factor in preparing students for future learning in work and life (Fawns et al., 2024). As a result of the increased use of generative AI and the emphasis on the graduate competency goals set by QBS, it was identified that engagement with professional practice was crucial to effectively deploying an authentic scenario-based assessment incorporating generative AI. Innovation in a taxation module Professional services firms were engaged at key points in the module delivery. The continuous assessment was jointly drafted with a professional practitioner to ensure that it simulated a ‘live’ case in professional practice. The integration of multiple taxes, the use of ChatGPT including the assessment of its accuracy and the need for students to use excel and the Lexis online database were embedded. Impact Students confirmed that they are much more informed about a tax career, that the ‘live’ case helped them become work-ready and fostered their professional identity aiding their transition to the labour market.
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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.036 | 0.012 |
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".