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Clicking some of the silly options: Exploring Player Motivation in Static and Dynamic Educational Interactive Narratives

Daeun Hwang, Samuel Shields, Alex Calderwood, Shi Johnson-Bey, Michael Mateas, Noah Wardrip-Fruin, Edward F. Melcer

TL;DR

The paper investigates how dynamic, AI-driven educational narratives affect learner motivation compared to static narratives by comparing Academical 1.0 (static) and Academical 2.0 (dynamic) in a RCR context. Using a between-subject design, it combines qualitative thematic analysis with ARC-based coding to assess engagement, autonomy, competence, and relatedness, revealing that dynamic narratives can enhance motivation when paired with responsive feedback and meaningful consequences, but risk looping and unclear progression. The study provides design implications for dynamic educational narratives (DENs), including strategies to pair content dynamism with progression signals, avoid repetitive dialogue hubs, and support role-playing to maintain engagement. Overall, the work advances understanding of AI-driven dynamic narratives in education and offers practical guidelines to balance pedagogical goals with narrative responsiveness for improved learning outcomes.

Abstract

Motivation is an important factor underlying successful learning. Previous research has demonstrated the positive effects that static interactive narrative games can have on motivation. Concurrently, advances in AI have made dynamic and adaptive approaches to interactive narrative increasingly accessible. However, limited work has explored the impact that dynamic narratives can have on learner motivation. In this paper, we compare two versions of Academical, a choice-based educational interactive narrative game about research ethics. One version employs a traditional hand-authored branching plot (i.e., static narrative) while the other dynamically sequences plots during play (i.e., dynamic narrative). Results highlight the importance of responsive content and a variety of choices for player engagement, while also illustrating the challenge of balancing pedagogical goals with the dynamic aspects of narrative. We also discuss design implications that arise from these findings. Ultimately, this work provides initial steps to illuminate the emerging potential of AI-driven dynamic narrative in educational games.

Clicking some of the silly options: Exploring Player Motivation in Static and Dynamic Educational Interactive Narratives

TL;DR

The paper investigates how dynamic, AI-driven educational narratives affect learner motivation compared to static narratives by comparing Academical 1.0 (static) and Academical 2.0 (dynamic) in a RCR context. Using a between-subject design, it combines qualitative thematic analysis with ARC-based coding to assess engagement, autonomy, competence, and relatedness, revealing that dynamic narratives can enhance motivation when paired with responsive feedback and meaningful consequences, but risk looping and unclear progression. The study provides design implications for dynamic educational narratives (DENs), including strategies to pair content dynamism with progression signals, avoid repetitive dialogue hubs, and support role-playing to maintain engagement. Overall, the work advances understanding of AI-driven dynamic narratives in education and offers practical guidelines to balance pedagogical goals with narrative responsiveness for improved learning outcomes.

Abstract

Motivation is an important factor underlying successful learning. Previous research has demonstrated the positive effects that static interactive narrative games can have on motivation. Concurrently, advances in AI have made dynamic and adaptive approaches to interactive narrative increasingly accessible. However, limited work has explored the impact that dynamic narratives can have on learner motivation. In this paper, we compare two versions of Academical, a choice-based educational interactive narrative game about research ethics. One version employs a traditional hand-authored branching plot (i.e., static narrative) while the other dynamically sequences plots during play (i.e., dynamic narrative). Results highlight the importance of responsive content and a variety of choices for player engagement, while also illustrating the challenge of balancing pedagogical goals with the dynamic aspects of narrative. We also discuss design implications that arise from these findings. Ultimately, this work provides initial steps to illuminate the emerging potential of AI-driven dynamic narrative in educational games.
Paper Structure (27 sections, 3 figures, 1 table)

This paper contains 27 sections, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. Dynamic Narrative Systems
  4. Narratives and Learning
  5. Academical
  6. Academical 1.0
  7. Academical 2.0
  8. Feature differences between version 1.0 and 2.0
  9. Method
  10. Procedure
  11. Participation
  12. Measurements
  13. Questionnaire
  14. Qualitative data analysis
  15. Results
  16. ...and 12 more sections

Figures (3)

  • Figure 1: Spectrum of storytelling in games. The circles represent plot points, and the solid arrows are hand-authored connections from one plot point to the next. The dotted arrows represent the plot points connected at runtime by system rules informed by designer goals and the history of player choice.
  • Figure 2: A screenshot of Academical 1.0 (left) next to 2.0 (right).
  • Figure 3: Choice options in StoryAssembler are chosen dynamically from the pool of available storylets. When selecting choices, the system looks at the precondition requirements for each choice (left) and attempts to find a storylet that satisfies that requirement. Additional preference is given to storylets that also accomplish story and/or pedagogical goals.