David Tensen

Generative AI (genAI) tools are increasingly used by doctoral candidates to obtain formative feedback, yet their reliability in thesis assessment remains unclear. This single-case study compared five genAI models: ChatGPT-o3, ChatGPT-5, ChatGPT-5 Thinking, Claude Opus 4.1, and Gemini 2.5 Pro, using five controlled prompts, including persona-based roles, to evaluate a doctoral confirmation thesis against Grainger’s F.A.C.T. rubric. Feedback varied by platform: Gemini 2.5 Pro produced uniform perfect scores (14/14), offering little diagnostic value, while GPT-5 Thinking and Claude Opus 4.1 were relatively stable, and ChatGPT-o3 and GPT-5 were more volatile. Persona prompts increased variability, reducing reliability compared to the neutral baseline. Alignment with the human reviewer was limited, with genAI feedback lacking the nuance, depth, and relational framing central to doctoral supervision. Friedman tests confirmed that Gemini’s ceiling effect drove the only significant omnibus result; excluding Gemini, no prompt- or model-level differences were significant. Descriptive effect sizes suggested moderate contrasts among non-Gemini models, but the small N constrained inference. We conclude that while genAI tools may supplement supervision by providing rapid feedback, they cannot replace human judgement. Responsible use requires feedback literacy training and safeguards against over-reliance.

This paper examines how artificial intelligence (AI) tools enhance feedback practices in doctoral education by providing a supplementary source of formative assessment. It explores the use of Generative AI alongside Grainger’s Formative Assessment Criteria-Based Tool (F.A.C.T.) in a single-subject case study of a pre-confirmation doctoral candidate at an Australian university. The study employs a naturalistic and interpretive approach with a sequential design, exploring interactions between a doctoral student and ChatGPT across multiple sessions where the AI tool evaluated a pre-confirmation thesis. Data collection included deidentified summarised feedback received from AI and an independent academic reviewer. Findings revealed that AI-generated feedback, guided by Grainger’s F.A.C.T. demonstrated thematic alignment with a human reviewer in identifying areas needing improvement, particularly regarding theoretical foundation and contribution to knowledge. However, the human reviewer provided contextually nuanced and discipline-specific feedback with more actionable suggestions. The study illustrates how, when coupled with formative rubrics, generative AI may serve as a supplementary feedback mechanism that may reduce power imbalances inherent in supervisor/reviewer-student relationships while providing expedient formative feedback. This research contributes to understanding how reflective practice in doctoral education may be enhanced through AI integration, addressing gaps in feedback literacy, socialisation, and standardised assessment parameters in doctoral contexts.

Humankind has not been here before. We are now living decades longer than our ancestors. We have never been healthier, wealthier and soul-stunted.As a result, we have an abundance of older people, but very few authentic elders.

Decenter Everything dares us to question the conventional script of later life that drifts from parenting to retirement to death. For the sake of the world, we must mature towards authentic eldering through intentional displacement; this means decentering anything that demands center-stage - including ourselves.

Through poetry, prose, research, and reflection Decenter Everything proposes five domains requiring decentering: God, being heard, knowledge, death, and consumption.
For anyone sensing the hollowness of our elderless culture, here lies a provocative path to reimagining a soulful and sustainable future.