Source-linked AI summary
Creation and Evaluation of a Pre-tertiary Artificial Intelligence (AI) Curriculum
Thomas K. F. Chiu, Helen Meng, Ching-Sing Chai, Irwin King, Savio Wong, Yeung Yam
TL;DR
Pre-tertiary AI curriculum design has limited prior evidence despite the growing importance of AI education. This study co-created and evaluated a Hong Kong secondary-school curriculum through collaboration among university experts and schools, finding improved student perceptions and teacher autonomy.
Problem
Pre-tertiary AI curriculum design has limited prior research, although AI education is increasingly important and has conventionally been taught at tertiary level.
Method
AI4Future co-created a modular AI curriculum with university experts and secondary-school educators, then evaluated it using student outcomes and teacher perceptions.
Results
Students showed significantly higher perceived AI knowledge, readiness, confidence, relevance, and intrinsic motivation after learning with the new curriculum.
Takeaways & Limitations
The co-creation process produced flexible learning resources, enhanced teachers’ AI knowledge, and fostered their autonomy in classroom implementation.
Abstract
from arXiv · showhide
Contributions: The Chinese University of Hong Kong (CUHK)-Jockey Club AI for the Future Project (AI4Future) co-created an AI curriculum for pre-tertiary education and evaluated its efficacy. While AI is conventionally taught in tertiary level education, our co-creation process successfully developed the curriculum that has been used in secondary school teaching in Hong Kong and received positive feedback. Background: AI4Future is a cross-sector project that engages five major partners - CUHK Faculty of Engineering and Faculty of Education, Hong Kong secondary schools, the government and the AI industry. A team of 14 professors with expertise in engineering and education collaborated with 17 principals and teachers from 6 secondary schools to co-create the curriculum. This team formation bridges the gap between researchers in engineering and education, together with practitioners in education context. Research Questions: What are the main features of the curriculum content developed through the co-creation process? Would the curriculum significantly improve the students perceived competence in, as well as attitude and motivation towards AI? What are the teachers perceptions of the co-creation process that aims to accommodate and foster teacher autonomy? Methodology: This study adopted a mix of quantitative and qualitative methods and involved 335 student participants. Findings: 1) two main features of learning resources, 2) the students perceived greater competence, and developed more positive attitude to learn AI, and 3) the co-creation process generated a variety of resources which enhanced the teachers knowledge in AI, as well as fostered teachers autonomy in bringing the subject matter into their classrooms.
I. INTRODUCTION
AI4Future addressed the limited development of pre-tertiary AI curricula by bringing university experts, schools, government, and industry into a Hong Kong co-creation process. The process produced adaptable curriculum content while supporting teacher learning and autonomy.
- Motivation: Pre-tertiary AI curriculum research remains limited, although AI education is increasingly considered important for preparing students for future study and careers.AI has conventionally been taught at tertiary level, leaving a gap in curriculum design and development for younger learners.
- Project formation: AI4Future brought together 14 professors and 17 principals and teachers from 6 secondary schools to co-create a junior-secondary AI curriculum in Hong Kong.The project also engaged CUHK faculties, the Education Bureau, and the local AI industry.
- Co-creation process: The co-creation process combined AI expertise ranging from fundamentals to ethics with teachers’ views on presenting content to junior-secondary students.This collaboration aimed to make the curriculum both substantive and accessible.
- Teacher development: Regular meetings with content presentations and discussions provided participating teachers with professional learning opportunities in pre-tertiary AI education.The teachers learned through collaboration with university members and other practitioners.
- Teacher autonomy: Iterative revisions across topics and modules created options for teachers to select and adapt, supporting teacher autonomy and centering students’ needs.The revisions integrated work across secondary and tertiary education over periods of weeks.
II. DRAWING REFERENCE TO K-12 ENGINEERING EDUCATION
The curriculum drew on K-12 engineering education to address the interdisciplinary, ethical, communicative, and technical demands of pre-tertiary AI learning. Its design also responds to challenges involving breadth, implementation resources, and variation across schools.
- Reference framework: K-12 engineering education recommends interdisciplinary content, impact and ethical considerations, technical communication, engineering thinking, and engineering techniques.These elements informed the project’s design of a pre-tertiary AI curriculum.
- Reference framework: Engineering education emphasizes giving students opportunities to apply mathematics, consider societal and ethical effects, communicate technical ideas, and design or troubleshoot solutions.Students are also expected to develop techniques, processes, and skills through practical tools and activities.
- Design challenges: The project identified three challenges: creating foundational yet broad AI content, translating the initiative into practice amid limited AI talent, and meeting differing school needs.The curriculum had to support students with varied interests, abilities, and future directions.
- Design response: A modular and reconfigurable structure was proposed to support flexible learning pathways across schools and allow teachers to adapt classroom activities to students’ capacities.This approach was intended to preserve teacher autonomy while accommodating local educational needs.
- Teacher autonomy: Teacher autonomy concerns the scope of teachers’ action and the curriculum’s provision of directions, resources, and rules that constrain or extend learning environments.It is positively related to perceived self-efficacy and job satisfaction, which are linked to teacher motivation and commitment.
III. CURRICULUM OVERVIEW
The curriculum framework organizes broad AI content into 12 chapters and five progressively deeper modules. This structure combines flexible selection with coverage of AI branches, applications, ethics, and societal impact.
- Framework structure: The framework contains 12 chapters spanning introductory concepts, AI branches, societal impact, ethical use, and the future of work.The chapters are designed to provide breadth and comprehensiveness.
- Flexible pathways: Teachers can select chapters according to classroom needs while retaining a coherent and self-contained curriculum.For example, a teacher may cover introductory and societal topics alongside selected AI branches.
- Module design: Each chapter uses five modules—Awareness, Knowledge, Interaction, Empowerment, and Ethics—organized across Beginner, Intermediate, and Advanced levels.The level-up design supports flexible content selection and progressive development of AI techniques and skills.
- Content coverage: The framework covers perceptual intelligence, language technologies, machine reasoning, simulation, creative generation, and AI-supported applications.The application coverage includes areas with societal implications, particularly for the future of work.
IV. CURRENT STUDY AND RESEARCH DESIGN
The study examined the curriculum’s content features, student outcomes, and teacher perceptions through research questions addressing flexibility, competence, attitudes, motivation, and teacher autonomy. The curriculum framework and its modules were presented as the basis for this examination.
- Research questions: RQ1 asks which main features characterize curriculum content developed through the co-creation process.The study specifically investigates the structure and resources produced for pre-tertiary AI education.
- Research questions: RQ2 asks whether the curriculum significantly improves students’ perceived competence, attitude, and motivation toward AI learning.These outcomes define the student-focused evaluation.
- Research questions: RQ3 asks about teachers’ perceptions of a co-creation process intended to foster teacher autonomy.The question focuses on how teachers experience and use the collaborative curriculum-development process.
- Curriculum framework: The curriculum framework is organized around chapters and modules, including Awareness, Knowledge, Interaction, Empowerment, and Ethics & Impact.These elements provide the curricular structure examined by the study.
B. Research Method
The study used a two-stage co-creation and implementation process, combining teacher adaptation with quantitative measures of students’ perceived competence, attitude, and motivation toward AI.
- Curriculum development and implementation: Professors authored technical content, then worked with 17 teachers in year-long biweekly meetings to refine outcomes and pedagogize the curriculum.The curriculum development stage was followed by implementation in which teachers adapted content to their school contexts and students’ needs.
- Curriculum development and implementation: Teachers selected and fine-tuned AI learning activities according to their schools’ cultures, environments, resources, and students’ needs and interests.Teaching occurred in blended online and face-to-face environments because of the COVID-19 pandemic.
- Student measures: Five six-point Likert-scale variables measured perceived AI knowledge, AI readiness, AI confidence, AI relevance, and intrinsic motivation to learn AI.The measures were adapted from previous studies and reported acceptable reliability and validity.
- Student measures: Perceived AI knowledge measured students’ self-perceived basic AI knowledge, while AI readiness measured comfort with everyday use of AI technologies.AI knowledge was newly proposed for this curriculum, whereas AI readiness was adopted from a previous study.
- Student measures: AI confidence measured confidence in learning AI content, AI relevance measured perceived usefulness of learning AI, and intrinsic motivation measured preference for curiosity-arousing AI topics.These measures used scales with reported reliability coefficients of α=.88, α=.91, and α=.74, respectively.
A. Research Question 1 – Main Features
The co-created curriculum offered flexible learning pathways and connected local AI examples to broader global understanding through varied activities, tools, and resources.
- Flexible learning pathways: The curriculum offered flexible learning pathways through modular, level-up design, varied examples, case studies, tools, and hands-on activities.Resources included Jupyter Notebooks, Blockly, WebAPPs, industry technologies, and the CUHKiCar robotic car with six built-in AI functions.
- Flexible learning pathways: Teachers could adapt tasks by selecting among multiple examples, including facial-recognition, chatbot, and World Cup prediction failures.These examples supported the task of explaining why AI technologies may not always work.
- Local and global understanding: The curriculum fostered local and global understanding by connecting students’ everyday experiences with AI’s societal and personal impacts.Activities moved from local explanations toward global understanding.
- Local and global understanding: Local applications such as KKbox and subway chatbots were connected with global applications such as Spotify.The examples were designed to establish student relevance and extend contextual understanding.
B. Research Question 2 – Student Enhancement
The evaluation found significant improvements across five student AI-related outcomes, while teacher feedback indicated that co-creation expanded AI knowledge and autonomy in classroom adaptation.
- Student outcomes: Five outcomes improved significantly: AIKG t(335)=8.01 (p<0.001), AIRD t(335)=3.45 (p<0.001), AICF t(335)=4.43 (p<0.001), AIRE t(335)=2.30 (p=0.003), and AIIM t(335)=2.82 (p=0.005).The outcomes were perceived AI knowledge, AI readiness, AI confidence, AI relevance, and intrinsic motivation to learn AI.
- Student outcomes: The project’s new AI curriculum enhanced students’ perceived AI knowledge, readiness, confidence, relevance, and intrinsic motivation to learn AI.The reported improvements covered all measured variables.
- Teacher perceptions: All participating teachers lacked formal AI training but learned more AI knowledge through co-creation and felt more qualified and confident to teach AI.Teacher feedback linked co-design activities with curriculum design knowledge and opportunities to try tools and discuss AI with others.
- Teacher perceptions: All teachers agreed that modular and level-up design enabled adaptation through selecting suitable examples, case studies, tools, and modules.This adaptability promoted teachers’ sense of autonomy in school-based teaching.
VI. DISCUSSIONS AND CONCLUSION
The AI4Future co-creation process connected university AI and education experts with secondary teachers, producing flexible, locally and globally oriented resources. The curriculum improved students’ perceived competence, attitudes, and intrinsic motivation toward AI while enhancing teachers’ AI knowledge and autonomy.
- The curriculum resources offered flexible learning pathways and fostered local and global understanding.
- The curriculum significantly enhanced students’ perceived competence, attitudes toward AI, and intrinsic motivation.Reported measures included AIKG, AIRD, AICF, AIRE, and AIIM.
- The co-creation process enhanced teachers’ AI competencies and fostered autonomy in shaping curriculum for their classrooms.It also functioned as a contemporary teacher professional development program.
B. Contributions of the AI4Future project
AI4Future transformed traditionally tertiary-level AI subject matter into junior secondary classrooms through co-creation, curriculum redesign, and teacher professional development. The project’s resources supported teachers’ knowledge and autonomy, while future work will examine adaptation across schools and teaching modes.
- AI4Future transformed traditionally tertiary-level AI subject matter into pre-tertiary junior secondary classrooms.
- The co-creation process redesigned and pedagogized AI content into varied classroom learning resources.
- The process enhanced teachers’ AI knowledge through sustained professional development and fostered teacher autonomy.
- Future work will track content selection and adaptation across students, interests, and face-to-face, online, and hybrid teaching modes.The project was entering its second year of pilot teaching and planned expansion to over thirty participating schools.
- Longitudinal research across more secondary schools could support learning analytics and future pedagogical development.
APPENDIX
The appendix presents pre- and post-questionnaire items assessing students’ AI knowledge and views about AI-enabled products and services.
- The questionnaire assessed students’ general knowledge of AI, including its uses, capabilities, creation, and applications.
- The questionnaire included items about whether AI technologies give people more control over their lives.
- The questionnaire assessed preferences for products and services using advanced AI technologies.