Curriculum Design
Constructing an AI Literacy Curriculum for Non-STEM Students: A TPACK-Based Approach in Tourism Management Education
This research proposes a specialized AI Literacy curriculum framework grounded in the TPACK model, tailored for undergraduate Tourism Management majors. It addresses the digital divide for non-STEM students by integrating theoretical cognition, practical tool application (ChatGPT, Midjourney), and ethical reflection into a 32-hour hybrid course. The study demonstrates enhanced student capacity to solve complex industry problems using AI tools and fostering human-AI collaboration mindsets, validated through pre-post survey data (n=87) and capstone projects.
Executive Impact
Key insights demonstrating the transformative potential for enterprise-wide AI literacy and adoption.
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Students with confidence gains
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Reduced technology anxiety
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AI Tool Confidence Effect Size (d)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Curriculum Design
AI Literacy Impact
Computational Innovations
TPACK Framework Validation
Effective TPACK as a robust framework for AI literacy education for non-STEM students, ensuring alignment of AI technologies (TK), tourism professional competencies (CK), and effective pedagogical strategies (PK).This finding aligns with recent research emphasizing student-driven innovation in generative AI adoption. The framework provided clear design principles: ensure AI tools are introduced alongside tourism applications (TK+CK), employ hands-on pedagogical strategies appropriate for non-technical learners (PK), and assess integrated knowledge application rather than isolated skills (TK+CK+PK).
Case A: Red Trace in Canton - Cultural Heritage Tourism Design
Overview: An innovative study tour integrating revolutionary history sites with contemporary urban cultural experiences in Guangzhou, employing a 'spatio-temporal folding' concept.
AI Application: ChatGPT for narrative structure and historical synthesis; AI-assisted research for site identification; Midjourney for conceptual visualizations (time-bridge graphics); AI-powered mapping for route optimization.
Learning Outcomes: Demonstrated high-level integration of AI for knowledge synthesis, sophisticated understanding of AI's creative potential, strong Content Knowledge (CK), and critical thinking (three-step verification protocol for factual accuracy, correcting AI hallucinations).
Case B: Bai Lan Healing Space - Data-Driven Market Analysis
Overview: Proposed a commercial tourism product for the 'Bai Lan' (lying flat) subculture among Gen Z, emphasizing low-effort healing experiences.
AI Application: AI-powered sentiment analysis (Xiaohongshu, Weibo) for psychological needs; ChatGPT for consumer personas; AI-enhanced brainstorming for product design (Five-Sense Healing System); competitive analysis and pricing strategy.
Learning Outcomes: Mastery of AI in Marketing and Consumer Insight; strategic tool employment for authentic market research; ethical AI awareness (discussing limitations, biases, privacy-conscious data collection, cultural bias awareness).
Case C: Smart Drive Future City - Technology Integration in Theme Parks
Overview: Proposed a theme park concept integrating autonomous driving technology with leisure tourism experiences, envisioning visitors experiencing AVs through themed attractions and experimental rides.
AI Application: AI tools for cross-disciplinary research (synthesizing AV tech with tourism design); ChatGPT for brainstorming innovative attraction concepts; AI-enhanced business planning tools for market feasibility, competitive analysis, financial projections; Midjourney for architectural renderings.
Learning Outcomes: Fostering AI+X innovation thinking; identification of a novel intersection between emerging tech and tourism; entrepreneurial mindsets; comprehensive business planning; critical evaluation of AI capabilities.
| Metric | Pre-Course (M, SD) | Post-Course (M, SD) | Effect Size (d) |
|---|---|---|---|
| AI Tool Confidence | 2.1 (0.8) | 4.3 (0.6) | 2.8 |
| Technology Anxiety | 3.8 (0.9) | 2.1 (0.7) | 2.1 |
| Perceived Professional Utility | 3.2 (0.8) | 4.5 (0.5) | 1.9 |
The results indicate statistically significant improvements across all measured dimensions, with large effect sizes providing strong empirical evidence. Students shifted from AI anxiety to AI agency, perceiving AI as a collaborative and empowering assistant. |
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Algorithmic Instructional Model
Phase 1: Cognitive Mapping (TK)
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Phase 2: Tool Integration (Tool Literacy)
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Phase 3: Contextual Application (PCK)
Transforms pedagogical workflows into executable computational processes for automated curriculum delivery and adaptive learning systems, emphasizing mastery-based learning through iterative feedback loops.
Calculate Your Potential AI Impact
Estimate the efficiency gains and cost savings your organization could achieve by integrating AI literacy.
Estimated Annual Savings
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Reclaimed Employee Hours (Annually)
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Your AI Literacy Implementation Roadmap
A strategic outline for integrating AI literacy into your organization, leveraging insights from this research.
Phase 1: Curriculum Development
Formulate TPACK-based modules, select AI tools, and design project-based learning activities.
Phase 2: Pilot Implementation
Conduct initial course delivery, gather student feedback, and refine instructional materials.
Phase 3: Quantitative Assessment & Iteration
Apply algorithmic evaluation models, statistical analysis, and data-driven adjustments for continuous improvement.
Phase 4: Scalability & Dissemination
Develop automated delivery systems and share findings for broader adoption in non-STEM education.
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