Enterprise AI Analysis
An Experiential Design Learning Model for AI Competency in Teacher Education
The increasing integration of artificial intelligence (AI) in education highlights the need for teacher preparation programs to support pre-service teachers in developing pedagogically grounded and ethically responsible Al competencies. This study designed and preliminarily examined an Experiential Design Learning model within a Digital Learning Ecosystem (EDL–DLE) to support the development of AI competencies and instructional innovation in pre-service science teacher education.
Executive Impact: Key Findings
A four-phase research and development framework was employed, including conceptual synthesis, model design and expert validation, implementation, and evaluation. Participants were 19 second-year pre-service science teachers from a university in Bangkok. Research instruments included a 40-item AI competency assessment and an instructional innovation evaluation rubric. Paired-sample t-test results indicated statistically significant pre-post difference across all AI competency dimensions, with large effect sizes (Cohen's d = 0.82–1.59), reflecting notable within-group changes observed within the EDL-DLE learning context. The instructional innovation lesson plans were evaluated as generally strong across multiple dimensions, particularly in learner-centered pedagogy, creativity, and collaboration, while relatively lower performance was observed in appropriate AI technology selection and ethical use. Overall, the findings provide preliminary evidence supporting the feasibility of the EDL-DLE model as an exploratory instructional approach for fostering foundational AI-related pedagogical competencies in pre-service science teacher education.
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Instructional Innovation Quality Achieved
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Deep Analysis & Enterprise Applications
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Experiential Design Learning (EDL)
Experiential Design Learning (EDL) synthesizes two widely used approaches in teacher education: Experiential Learning (EL) and Design Thinking (DT). EL conceptualizes learning as a cyclical process in which individuals engage with concrete experiences, reflect on those experiences, form new conceptual understandings, and apply those insights in new contexts (Kolb, 2015). Design Thinking complements this process by guiding learners to explore problems empathetically, generate ideas, construct prototypes, and iteratively refine instructional solutions (IDEO, 2015; Morris, 2020). When integrated, these two approaches form the foundation of EDL, where experience-driven sense-making supports the design of creative and context-appropriate solutions. The EDL process encourages learners to investigate authentic educational problems, develop emerging insights, and construct prototype solutions that can be iteratively refined (Butler et al., 2019; Ajani, 2023). Within pre-service science teacher education, EDL has been shown to foster inquiry, creativity, and instructional adaptability—competencies increasingly required in digitally mediated learning environments. However, while EDL has been explored in various instructional design contexts, its use in AI-supported teaching and learning has only recently begun to receive attention. Prior research has indicated that experiential and design-oriented learning processes—characterized by problem exploration, iterative design, and reflective refinement—can promote learner agency and higher-order competencies within digital learning contexts (Techakosit et al., 2025).
The EDL process unfolds through five iterative stages: Empathic Exploration (understanding user needs), Reflective Framing (defining problems), Creative Ideation (generating solutions), Applied Prototyping (building preliminary solutions), and Iterative Testing (refining solutions based on feedback).
Digital Learning Ecosystem (DLE)
The Digital Learning Ecosystem (DLE) provides a broader lens for understanding how learning unfolds within technology-rich environments. Rather than focusing solely on digital tools, DLE frameworks conceptualize learning as emerging from dynamic interactions among people, technologies, data, and sociocultural contexts (Thorneycroft, 2022; Rojas & Chiappe, 2024). Across the literature, key components include digital infrastructure, collaborative learning communities, adaptive pedagogical design, and AI-enabled personalization (Nguyen & Tuamsuk, 2022; Martelo Gómez et al., 2024).
This study synthesizes DLE components into four core areas: Digital Infrastructure (learning platforms, data integration), Human Engagement (collaborative learning communities), AI Personalization (AI recommendation systems, learning analytics), and Adaptive Pedagogy (learner-centered design, adaptive content). These elements create an integrated environment supporting experiential and design-oriented learning.
AI Competencies
Al competencies refer to the knowledge, skills, and dispositions required to understand, apply, and critically evaluate artificial intelligence in educational settings (Ng et al., 2021; UNESCO, 2024). Recent frameworks converge on four major areas: foundational AI knowledge, practical application, ethical and responsible use, and collaboration with AI systems and other learners (Chiu et al., 2024; Annapureddy et al., 2025). These dimensions are increasingly relevant to future science teachers, who now rely on AI to support lesson design, learner analytics, instructional decision-making, and content creation. Current research suggests that experiential and design-oriented learning can promote deeper Al competency development by placing learners in authentic problem scenarios where AI tools serve meaningful instructional purposes (Filo et al., 2024; Annapureddy et al., 2025). However, such learning must occur within environments that support experimentation, feedback, and ethical reflection—conditions closely aligned with the principles of the DLE. While these frameworks provide valuable guidance, recent discussions have increasingly emphasized the importance of examining how experiential and design-oriented learning can be supported by ecosystem-level conditions in the development of AI competencies for teacher education. In teacher education, AI competency is increasingly viewed as a professional capability that supports instructional design, assessment, and feedback rather than as a replacement for teachers' pedagogical roles. Studies in educational contexts suggest that AI can function as a complementary tool that enhances teaching practices while preserving teachers' agency and professional judgment (Navío-Inglés et al., 2025).
The four key AI competency dimensions explored in this study are: AI Knowledge (understanding core concepts), AI Application (using AI to solve problems), AI Ethics (responsible and transparent use), and AI Collaboration (human-AI teamwork and knowledge sharing).
Instructional Innovation
Instructional innovation refers to the design of learner-centered and pedagogically sound learning experiences that enhance learning effectiveness and student satisfaction (Y.-J. Lee, 2011). The TPACK framework (Mishra & Koehler, 2006) provides a foundational perspective for examining instructional innovation by emphasizing the dynamic integration of content, pedagogy, and technology. Within this framework, instructional innovation is shaped not only by teachers' technological pedagogical content knowledge but also by their pedagogical beliefs and instructional design decisions (Almunawaroh & Steklács, 2025). In science education, TPACK has been shown to support inquiry-oriented learning through technology-enhanced environments such as simulations and computer-based laboratories (Srisawasdi, 2012).
In this study, instructional innovation was primarily manifested through the development of lesson plans that emphasized learner-centered pedagogical principles, including the promotion of student creativity, facilitation of collaboration, and alignment between learning objectives, activities, and assessment strategies, rather than merely adopting digital tools.
20.1%
Increase in Overall AI Competency Scores Post-Intervention (Cohen's d = 1.59)
Source: Table 9, Overall mean score increase from 157.05 to 188.63
Experiential Design Learning (EDL) Process Flow
Empathic Exploration
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Reflective Framing
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Creative Ideation
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Applied Prototyping
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Iterative Testing
| Dimension | Strengths (Mean > 3.0) | Areas for Development (Mean <= 3.0) |
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| Learner-Centered Pedagogy |
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| AI Technology Integration & Ethics |
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| Source: Table 10: Evaluation of Instructional Innovation Lesson Plans | ||
Case Study: Bridging Theory to Practice in Pre-service Science Teacher Education
Introduction: This study demonstrates how the EDL-DLE model successfully fostered AI competencies and instructional innovation among pre-service science teachers by embedding AI use within experiential and design-oriented instructional activities. The approach moved beyond isolated technical content to integrate AI as a pedagogical resource.
Challenge: Pre-service teachers often struggle to translate AI-related knowledge into pedagogically grounded instructional practice, frequently aligning AI use with technical convenience rather than learning objectives or ethical considerations.
Solution: The EDL-DLE model, delivered over five weeks in a blended format, engaged 19 second-year pre-service science teachers in authentic instructional problem-solving tasks. It utilized a digital learning ecosystem for iterative design, collaborative spaces, and feedback mechanisms. This context allowed AI to function as a pedagogical resource, supporting reasoning, reflection, and iterative refinement of lesson plans.
Outcome: Statistically significant improvements were observed across all four AI competency dimensions (AI Knowledge, AI Application, AI Ethics, AI Collaboration), with large effect sizes (Cohen's d = 0.82–1.59). Instructional innovation outputs were rated highly, especially in learner-centered pedagogy (creativity, collaboration, clear objectives). While ethical AI use and technology selection showed comparatively lower, but still 'good', performance, overall results support the model's feasibility in fostering foundational AI-related pedagogical competencies.
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Our AI Integration Roadmap
Our proven methodology guides your enterprise through a structured journey to harness the full potential of AI.
Discovery & Strategy
We begin with a deep dive into your current educational practices, identifying key challenges and strategic opportunities for AI integration within your teacher training programs. This phase establishes a clear vision and roadmap.
Model Design & Customization
Based on the discovery, we adapt and customize the EDL-DLE model to fit your institutional context, ensuring alignment with curriculum goals and technological infrastructure. This includes selecting appropriate AI tools and designing specific learning activities.
Pilot Implementation & Iteration
The customized model is rolled out in a pilot program with a select group of pre-service teachers. We gather feedback, analyze performance data, and make iterative refinements to optimize the learning experience and AI competency development.
Full-Scale Deployment & Training
After successful piloting, we support the full-scale deployment across your institution, providing comprehensive training for faculty and ongoing technical and pedagogical support to ensure seamless adoption and sustainable impact.
Performance Monitoring & Optimization
We establish robust monitoring frameworks to track AI competency development and instructional innovation outcomes. Continuous analysis and optimization ensure long-term effectiveness and evolving pedagogical relevance.
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