Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Actor's Note: Examining the Role of AI-Generated Questions in Character Journaling for Actor Training

Sora Kang, Jaemin Zoh, Hyoju Kim, Hyeonseo Park, Hajin Lim, Joonhwan Lee

TL;DR

Actor's Note is designed, a journaling tool that tailors questions to the script, role, and rehearsal phase while preserving actor agency and contributes empirical insights and design principles for creativity-support tools that sustain reflective practices while preserving artistic immersion in performance training.

Abstract

Character journaling is a well-established exercise in actor training, but many actors struggle to sustain it due to cognitive burden, the blank page problem, and unclear short-term rewards. We reframe large language models not as co-authors but as maieutic partners-tools that guide reflection through context-aware questioning rather than producing text on behalf of the user. Based on this perspective, we designed Actor's Note, a journaling tool that tailors questions to the script, role, and rehearsal phase while preserving actor agency. We evaluated the system in a 14-day crossover study with 29 actors using surveys, logs, and interviews. Results indicate that the tool reduced entry barriers, supported sustained reflection, and enriched character exploration, with participants describing different benefits when AI was introduced at earlier versus later rehearsal stages. This work contributes empirical insights and design principles for creativity-support tools that sustain reflective practices while preserving artistic immersion in performance training.

Actor's Note: Examining the Role of AI-Generated Questions in Character Journaling for Actor Training

TL;DR

Actor's Note is designed, a journaling tool that tailors questions to the script, role, and rehearsal phase while preserving actor agency and contributes empirical insights and design principles for creativity-support tools that sustain reflective practices while preserving artistic immersion in performance training.

Abstract

Character journaling is a well-established exercise in actor training, but many actors struggle to sustain it due to cognitive burden, the blank page problem, and unclear short-term rewards. We reframe large language models not as co-authors but as maieutic partners-tools that guide reflection through context-aware questioning rather than producing text on behalf of the user. Based on this perspective, we designed Actor's Note, a journaling tool that tailors questions to the script, role, and rehearsal phase while preserving actor agency. We evaluated the system in a 14-day crossover study with 29 actors using surveys, logs, and interviews. Results indicate that the tool reduced entry barriers, supported sustained reflection, and enriched character exploration, with participants describing different benefits when AI was introduced at earlier versus later rehearsal stages. This work contributes empirical insights and design principles for creativity-support tools that sustain reflective practices while preserving artistic immersion in performance training.
Paper Structure (63 sections, 5 figures, 12 tables)

This paper contains 63 sections, 5 figures, 12 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Actor Training and Character Journaling
  4. LLMs as Creative and Performative Partners
  5. Digital Journaling and Reflection Systems
  6. Formative Study
  7. Prototype
  8. Procedure
  9. Findings
  10. Actor's Note
  11. System Overview and Interaction Flow
  12. Question Generation
  13. System Implementation
  14. Method
  15. Study Design
  16. ...and 48 more sections

Figures (5)

  • Figure 1: Early web prototype used in the formative study.
  • Figure 2: Onboarding flow, from script upload and role selection to auto-generated character profile and dashboard access.
  • Figure 3: Daily flow, from receiving stage-aware questions to writing, saving, and archiving journal entries.
  • Figure 4: Temporal trajectories by group. Scores increased during AI phases (except Cognitive Burden), showing comparable magnitudes across groups.
  • Figure 5: Post-study survey results showing (a) correlations between user perception metrics and (b) residual effects of AI usage on recall and self-generation. Histograms indicate a positive lasting impact of the tool.