DEVELOPING PROBLEM-SOLVING COMPETENCY FOR STUDENTS THROUGH THE CARE-KNOW-DO PEDAGOGICAL MODEL AND UNESCO'S AI COMPETENCY FRAMEWORK IN CHEMISTRY TEACHING
DOI:
https://doi.org/10.18173/2354-1075.2025-0120Keywords:
problem-solving competency, Care-Know-Do pedagogical model, UNESCO’s AI competency framework for students, chemistry educationAbstract
In the context of rapid global digital transformation, developing students' problem-solving competency has become an urgent goal in modern education. This study proposes integrating the Care-Know-Do pedagogical model with UNESCO’s AI Competency Framework for Students to enhance problem-solving competency in Chemistry education. Through theoretical analysis, situational surveys, and model design, the research establishes an instructional framework that enables students to connect Chemistry knowledge with real-world issues while using AI to support analysis, evaluation, and creative problem-solving. The Care-Know-Do model fosters ethical values (Care), consolidates academic knowledge (Know), and cultivates creative action skills (Do), whereas the AI competency framework for students equips students with digital thinking across three levels: Understand, Apply, and Create. Survey results reveal that Chemistry teachers have a positive perception of AI's role in education, but lack clear implementation guidelines. Therefore, the study proposes an integrated pedagogical framework along with illustrative examples to support teachers in designing lessons that effectively foster students’ problem-solving competency.
Downloads
References
[1] Bộ Giáo dục và Đào tạo, (2018). Chương trình tổng thể Giáo dục phổ thông 2018.
[2] ĐTQ Mai, TT Phượng, ĐT Anh, VT Quỳnh, NTM Anh & BTY Hằng, (2024). Thiết kế và sử dụng trò chơi bảng trong dạy học chủ đề “Phản ứng hóa học” nhằm phát triển năng lực giải quyết vấn đề cho học sinh. HNUE Journal of Science, 69(1), 217-226. https://doi.org/10.18173/2354-1075.2024-0038.
[3] Peters MA & Green BJ, (2024). Wisdom in the age of AI education. Postdigital Science and Education, 6(4), 1173-1195. https://doi.org/10.1007/s42438-024-00460-w.
[4] Pandit S, Sarkar SK, Barik S & Sahu S, (2024). AI in skill development, critical thinking, digital literacy, and AI-driven job preparation. In Impacts of AI on Students and Teachers in Education 5.0, IGI Global Scientific Publishing, 23-76. https://doi.org/10.4018/979-8-3693-8191-5.ch002.
[5] Okada A & Gray P, (2023). A climate change and sustainability education movement: Networks, open schooling, and the “Care-Know-Do” framework. Sustainability, 15(3), 2356. DOI: 10.3390/su15032356.
[6] Okada A, Panselinas G, Bizoi M, Malagrida R & Torres PL, (2024). Fostering transversal skills through open schooling with the Care-Know-Do framework for sustainable education. Sustainability, 16(7), 2794.
[7] Freire P, (2020). Pedagogy of the oppressed. The Community Performance Reader, 24-27. https://doi.org/10.4324/9781003060635-5.
[8] Okada A & Sheehy K, (2020). Factors and recommendations to support students’ enjoyment of online learning with fun: A mixed-method study during COVID-19. Frontiers in Education, 5(1), 584351.
[9] Okada A, (2024). A self‐reported instrument to measure and foster students' science connection to life with the CARE‐KNOW‐DO model and open schooling for sustainability. Journal of Research in Science Teaching, 61(10), 2362-2404. https://doi.org/10.1002/tea.21964.
[10] UNESCO, (2024). AI competencies framework for K-12 education. https://www.unesco.org/en/articles/ai-competency-framework-students.
[11] Kale MP, (2025). Exploring the impact of artificial intelligence on education challenges and opportunities for sustainable development. In Revolutionizing Education With Remote Experimentation and Learning Analytics, 133-142. DOI: 10.4018/979-8-3693-8593-7.ch009.
[12] Dumin L, (2025). Preparing students to live and work in an AI-driven world. Teaching and Learning in the Age of Generative AI, 253-279. https://doi.org/10.4324/9781032688602-16.
[13] Okada A, Sherborne T, Panselinas G & Kolionis G, (2025). Fostering transversal skills through open schooling supported by the Care-Know-Do pedagogical model and the UNESCO AI competencies framework. International Journal of Artificial Intelligence in Education, Springer. DOI: 10.1007/s40593-025-00458-w.
[14] VT Hiền & TT Ninh, (2016). Increasing students’ problem-solving competency through the use of integrated teaching about compounds of sulfur, which produce acid rain. Journal of Science, Educational Science, 61, 54-65. https://doi.org/10.18173/2354-1075.2016-0069.
[15] Wang Y, Liu C & Tu TF, (2023). Factors affecting the adoption of AI-based applications in higher education: an analysis of teachers' perspectives using structural equation modeling. Educational Technology & Society, 24(3), 116-129. https://www.jstor.org/stable/27032860.
[16] Furze L, Perkings M, Roe J & MacVaugh J, (2024). The AI Assessment Scale (AIAS) in action: A pilot implementation of GenAI-supported assessment. Australasian Journal of Educational Technology, 40(4), 38-55. https://doi.org/10.14742/ajet.9434.
[17] Hmoud M, Swaity H, Anjass E & Aguaded-Ramirez EM, (2024). Rubric development and validation for assessing tasks' solving via AI chatbots. Electronic Journal of e-Learning, 22(6), 1-17. https://doi.org/10.34190/ejel.22.6.3292.
[18] NV Biên & LHM Ngân, (2020). Xây dựng khung năng lực giải quyết vấn đề trong giáo dục khoa học robot cho học sinh Trung học cơ sở. HNUE Journal of Science, 65(7), 184-196. https://doi.org/10.18173/2354-1075.2020-0089.
