Teaching artificial intelligence in extracurricular contexts through narrative-based learnersourcing

Title of the Paper

Teaching Artificial Intelligence in Extracurricular Settings through Narrative-Based Learning Crowdsourcing

Paper Information

• Subject Area: AI education, particularly using narrative approaches and technology-enhanced learning crowdsourcing platforms to teach AI.
• Keywords: AI literacy, STEM education, digital storytelling, collaborative learning, online learning tools, learning crowdsourcing

Research Background and Issues

• Identified Problems or Challenges:

  1. Artificial Intelligence (AI) knowledge is becoming a critical component of 21st-century education, yet AI educational resources remain extremely limited, especially for the K-12 level.
  2. Integrating AI education into traditional classroom environments faces challenges, including teachers' lack of understanding of AI and the limitations of standardized curricula.
  3. A core issue is how to effectively provide accessible and meaningful AI learning resources to students from diverse backgrounds.

• Significance:

  1. AI education empowers students to tackle complex problems in an AI-driven world, enhances computational thinking, and enables responsible evaluation of technology.
  2. It can deepen students' interest in STEM fields, particularly for traditionally underrepresented groups such as women and minorities.

• Research Motivation and Related Work: This paper aims to explore how collaborative learning technologies (e.g., interactive narrative-based learning crowdsourcing platforms) can effectively teach AI knowledge. It references existing educational theories (e.g., Bloom's taxonomy of cognitive domains), the benefits of narrative learning, and the potential of learning crowdsourcing.

Proposed Solution

• Proposed Method/Solution:

  1. Introduce a narrative-based learning crowdsourcing platform where students participate in learning by designing interactive "choose-your-own-adventure" stories.
  2. The platform innovatively combines narrative learning with learning crowdsourcing to create a user-driven educational ecosystem.
  3. The system structures the content generation process through multi-level learning tasks and employs a tiered "learner role" design (Explorer, Builder, Facilitator) to gradually deepen student engagement.

• Innovative Aspects:

  1. For the first time, the concepts of narrative and learning crowdsourcing are combined in AI education, making complex technical topics more intuitive.
  2. Emphasis on cultural sensitivity by embedding learners' cultural backgrounds to make the content more engaging.
  3. Clear processes and tools support learners with low technical backgrounds to gradually become creators, promoting the sustainability of the learning ecosystem.

• Implementation Steps and Key Technologies:

  1. Use narrative templates and content guidelines to progressively introduce learners to narrative design.
  2. The platform comprises three key components:
     • Story Adventure Component: Allows learners to choose and experience interactive story content.
     • Story Graph: Provides a graphical interface to analyze story structures, supporting content creation and design.
     • Story Infrastructure: Includes a full-stack codebase, analytics interface, and support documentation.
  3. Utilizes a Node.js backend and integrates TypeScript to help learners design and implement interactive content.

Research Outcomes

• Specific Outcomes:

  1. Knowledge Growth: Users' AI knowledge increased by an average of 24.2% from pre- to post-participation.
  2. Interest Stimulation: Learners showed a greater inclination to explore advanced AI topics and re-engage in learning through story design.
  3. Sense of Community: The platform successfully fostered a learning community, with learners exhibiting positive collaboration and mutual support behaviors.
  4. Impact on Diverse Groups: Particularly effective for women and learners with limited technical backgrounds, enhancing their self-efficacy and interest in AI.

• Advantages (Compared to Existing Solutions):

  1. The platform can be easily extended to learners from different cultural backgrounds, achieving "culturally sensitive storytelling."
  2. Supports novice learners in quickly participating in story creation and technical content development.
  3. Addresses key challenges in traditional learning crowdsourcing systems regarding participation motivation and content quality.

• Experimental or Evaluation Results:

  1. In a one-week study, platform users (N=27) demonstrated significant growth in AI knowledge and increased interest.
  2. Expert reviews of learner-generated content rated its correctness and helpfulness at 3.91 and 3.38 out of 5, respectively.
  3. Over 100 new users joined the platform through learner recommendations in a short period, demonstrating the platform's potential for organic growth.

• Limitations and Future Directions:

  1. The platform currently relies on an initial content set, and more advanced automation methods are needed to address the "cold start" problem.
  2. The short study duration does not capture potential long-term learning behavior evolution, necessitating longitudinal research.
  3. Further exploration is needed on how to better balance content quality and user-driven innovation in collaborative learning.