1. The Construction of Innovative Theories and Systematic Practical Exploration of New Happy Education

• Keywords: 

  New Happy Education, Game-based Learning, Learning Sciences, Construction of Theories, Systematic Practice

• Executive Summary: 

  New Happy Education aims to integrate learning sciences, game-based learning, artificial intelligence, and other emerging technologies and learning approaches to make learning more scientific, more enjoyable, and more effective. As educational reform enters a deep-water zone, it is essential to recognize that learning lies at the heart of education. Transforming learning is therefore key to driving deeper changes in education. Only through such transformation can we address persistent issues, such as low student motivation and increasing psychological challenges among learners.

• Project Description

  This project was inspired by the applicant’s long-term exploration of how to make learning more engaging and meaningful. Since 2004, the applicant has researched game-based learning, aiming to improve students’ learning experiences and motivation under exam-oriented education. However, it became evident that relying solely on games risked turning “happy education” into entertainment, overlooking learning effectiveness. In response, the applicant integrated learning sciences with game-based learning, later leading a national project titled Game-Based Learning from the Perspective of Learning Sciences. With the rapid rise of artificial intelligence, AI was gradually incorporated into the framework.  This evolution led to the theory of New Happy Education—an approach grounded in learning sciences, characterized by game-based learning, and supported by AI. It also draws on brain science, virtual reality, and other emerging technologies to promote learning that is scientific, joyful, and effective, helping every child grow up in a healthy, balanced way. The project seeks to break the dilemma of “sacrificing joy for achievement” or“choosing fun over growth.” Through thoughtful learning design, students can experience real happiness in learning—not just fun in playing—and achieve holistic development through genuine engagement.

2. Development and Validation of the PA-SDA Scale for AI-Integrated Self-Directed Language Learning

• Keywords: 

  Artificial intelligence; Self-directed learning; Scale development; Personal attributes

• Executive Summary: 

  The Personal Attributes for Self-Directed AI Learning (PA-SDA) Scale is the first validated quantitative instrument measuring key personal attributes of language learners in AI-integrated self-directed learning contexts. Grounded in the AI-Integrated SDL Framework (Li et al., 2024b), the 44-item scale was developed and validated with 699 global language learners using generative AI tools such as ChatGPT. It offers educators, researchers, and policymakers a robust diagnostic tool to assess learners’ readiness for AI-enhanced education, enabling targeted interventions, equitable access, and strategic pedagogical design. The PA-SDA Scale bridges theory and practice, providing a scalable and adaptable solution for AI-era education worldwide.

• Project Description:

  This project was inspired by a pressing gap in education: the absence of validated tools to assess learners’ readiness for AI-integrated self-directed learning (SDL). While generative AI tools are rapidly transforming language education, success depends not only on technology but on learners’ ability to navigate, adapt, and critically engage with AI systems. Existing SDL readiness scales were developed before the advent of advanced AI, overlooking new competencies such as AI-specific strategy use, resource management, and critical evaluation of AI-generated content. Building on Li et al.’s (2024b) AI-Integrated SDL Framework, we operationalized five personal attributes (Attitude, Strategy Use, Motivation, Self-Efficacy, and Resource Use) into measurable items. Following rigorous scale development protocols, we conducted exploratory and confirmatory factor analyses (EFA, CFA) to validate the construct structure. The final 44-item PA-SDA Scale reflects nine subconstructs, offering fine-grained insight into learner readiness in AI-mediated contexts. The PA-SDA addresses the UN Summit of the Future 2024 agenda by advancing inclusive, quality digital education. It empowers educators to design interventions that bridge the AI readiness gap, supports equitable access to AI-enhanced learning, and informs policy on AI integration in curricula. Its adaptability allows application across languages, age groups, and educational levels. Ultimately, this project contributes a theoretically grounded, empirically validated innovation that will help education systems worldwide prepare learners for the future of AI-enabled learning.