Algorithmic Self-Deception: How AI-Generated Feedback Skews Learners' Self-Reflection

This is the author’s self-archived published version of an article originally published in Cognizance Journal of Multidisciplinary Studies. PUBLISHED: at https://doi.org/10.47760/cognizance.2025.v05i07.026. Since two DOIs exist (one from cognizance and another from zenodo), use citation below.

Official citation (please use this when citing):

Besigomwe, K. (2025). Algorithmic Self-Deception: How AI-Generated Feedback Skews Learners’ Self-Reflection. Cognizance Journal of Multidisciplinary Studies, 5(7), 333–342. https://doi.org/10.47760/cognizance.2025.v05i07.026

Abstract:

This study investigated how artificial intelligence (AI)-generated feedback from large language models like
ChatGPT affected undergraduate students’ self-assessment accuracy and reflective depth. Conducted with 180
students from Makerere University (Uganda) and the University of Cape Town (South Africa), the research
explored the impact of AI feedback compared to human and no feedback, focusing on self-assessment accuracy,
reflection quality, and cross-cultural differences. The objectives were to: (i) assess the impact of AI feedback on
self-assessment accuracy; (ii) measure its effect on reflection depth and quality; and (iii) compare responses
between Makerere and UCT students, considering cultural and contextual factors. Using a mixed-methods
experimental design, participants were randomly assigned to receive AI feedback, human feedback, or no
feedback on essays. Quantitative data analyzed with ANOVA and t-tests showed that AI feedback improved
essay scores (mean 82.5%) significantly over human feedback (80.1%) and no feedback (73.2%). However, AI
feedback led to greater overconfidence, evidenced by higher calibration errors (t(58) = 3.28, p = .002), and
produced reflections of lower quality compared to human feedback (F(2,177) = 26.4, p < .001). Qualitative
analysis revealed that Makerere students tended to trust AI feedback more and exhibited stronger
overconfidence than their UCT counterparts. Findings highlighted an ―algorithmic self-deception‖ effect, where
AI-generated feedback’s vague positivity inflated learners’ self-perceptions and diminished critical reflection.
The study recommended incorporating AI literacy training, developing hybrid human–AI feedback models, and
designing culturally sensitive AI tools to foster deeper metacognitive engagement. These strategies were vital to
harness AI’s educational potential while addressing its limitations across diverse cultural settings.
Keywords: Artificial Intelligence (AI) feedback, algorithmic self-deception, metacognition, reflection quality,
automated writing tools

Copyright and publication note:

This article was originally published in Cognizance Journal of Multidisciplinary Studies in 2025.

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