Empowering Authorship with AI: a Novel Academic Writing Technology for Authorial Voice
Bibliographic record
Abstract
This study investigates the integration of Technology-Enhanced Learning (TEL) and Generative Artificial Intelligence (GAI) to improve the development of authorial voice in English as a Second Language (ESL) students’ academic writing. Specifically, it evaluates AVATAR, an intelligent tutoring system designed to support both the performance and authorship belief aspects of authorial voice in argumentative essays. Conducted with 23 first-year ESL students at a Fijian university, this proof-of-concept study used a mixed-methods approach, including pre- and post-use surveys, interviews, and analysis of student essays. Participants engaged with AVATAR while working on an argumentative essay, and the system’s affordances were assessed through changes in authorial voice expressions and students’ self-perceptions of their authorship. The findings showed a statistically significant improvement in both the authorship beliefs and performance of authorial voice. Positive changes in authorship beliefs were evident in participants’ increased self-belief in their authorial voice, as reflected in higher post-use authorship survey results and increased self-scores participants gave themselves when reflecting on their authorial voice. In terms of performance, there was a significant increase in the authorial voice strength scores given to the essays participants edited in AVATAR. However, some participants expressed a need for additional teacher feedback to further validate their progress. This study underscores the potential of GAI to support holistic authorial development and offers insights for designing academic writing technologies that focus on both the product and the reflective processes involved in 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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".