From Pen to Prompt: A Collaborative Autoethnography on Using ChatGPT-4 to Process Procrastination through AI-Mediated Journaling

Document Types

Paper Presentation

Research Theme (for Paper Presentation and Poster Presentation submissions only)

21st Century Learning and Innovations (CLI)

School Name

De La Salle University, Manila

Track or Strand

Humanities and Social Science (HUMSS)

Research Advisor (Last Name, First Name, Middle Initial)

Alemania, Belle Beatriex’ M.

Start Date

25-6-2026 10:30 AM

End Date

25-6-2026 12:00 PM

Zoom Link/ Room Assignment

Online - https://zoom.us/j/91936856247?pwd=oCMfMsh44I2wb0dYsEgoInDJy59bOq.1 Meeting ID: 919 3685 6247 | Passcode: research

Abstract/Executive Summary

Existing literature on academic procrastination has largely focused on psychological, behavioral, and motivational determinants using traditional self-report measures and intervention-based approaches. However, we observed a notable gap in how generative artificial intelligence (AI) can function as an active reflective partner in journaling processes. While reflective writing is widely recognized as a tool for self-awareness and regulation, there is limited understanding of how AI-mediated journaling influences meaning-making. Addressing this gap, the emergence of generative AI has expanded the possibilities of reflective writing by introducing conversational tools capable of responding to emotional and cognitive experiences in real time. In this study, we examined how AI-mediated journaling supports self-reflection in relation to academic procrastination. Using a collaborative autoethnographic approach, we documented experiences of procrastination, which we then processed through interactions with ChatGPT-4. This allowed us to compare human-written reflections with AI-assisted responses and identify shifts in interpretation and meaning-making. We analyzed the data thematically, focusing on emotional states, cognitive patterns, avoidance behaviors, coping strategies, and the transformations emerging from AI-mediated reflection. Findings showed that initial journal entries were characterized by guilt, anxiety, emotional fatigue, task avoidance, and self-blame. In contrast, AI-mediated reflections reframed these experiences through cognitive-behavioral lenses such as emotional avoidance, task difficulty appraisal, and distorted urgency perceptions. The AI also introduced structured coping strategies, including micro-tasking, behavioral activation, time management, and value-based motivation. Overall, we observed a shift from emotion-centered self-blame toward structured understanding and actionable coping strategies by identifying emotional triggers, procrastination cycles, and personal values.

Keywords

ChatGPT; collaborative autoethnography; journaling; procrastination; self-reflection

Statement of Originality

yes

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Jun 25th, 10:30 AM Jun 25th, 12:00 PM

From Pen to Prompt: A Collaborative Autoethnography on Using ChatGPT-4 to Process Procrastination through AI-Mediated Journaling

Existing literature on academic procrastination has largely focused on psychological, behavioral, and motivational determinants using traditional self-report measures and intervention-based approaches. However, we observed a notable gap in how generative artificial intelligence (AI) can function as an active reflective partner in journaling processes. While reflective writing is widely recognized as a tool for self-awareness and regulation, there is limited understanding of how AI-mediated journaling influences meaning-making. Addressing this gap, the emergence of generative AI has expanded the possibilities of reflective writing by introducing conversational tools capable of responding to emotional and cognitive experiences in real time. In this study, we examined how AI-mediated journaling supports self-reflection in relation to academic procrastination. Using a collaborative autoethnographic approach, we documented experiences of procrastination, which we then processed through interactions with ChatGPT-4. This allowed us to compare human-written reflections with AI-assisted responses and identify shifts in interpretation and meaning-making. We analyzed the data thematically, focusing on emotional states, cognitive patterns, avoidance behaviors, coping strategies, and the transformations emerging from AI-mediated reflection. Findings showed that initial journal entries were characterized by guilt, anxiety, emotional fatigue, task avoidance, and self-blame. In contrast, AI-mediated reflections reframed these experiences through cognitive-behavioral lenses such as emotional avoidance, task difficulty appraisal, and distorted urgency perceptions. The AI also introduced structured coping strategies, including micro-tasking, behavioral activation, time management, and value-based motivation. Overall, we observed a shift from emotion-centered self-blame toward structured understanding and actionable coping strategies by identifying emotional triggers, procrastination cycles, and personal values.

https://animorepository.dlsu.edu.ph/conf_shsrescon/2026/BoA_CLI/6