| CODE | TEM5019 | ||||||||||||||||
| TITLE | Thinking with AI - A Computational Thinking Approach | ||||||||||||||||
| UM LEVEL | 05 - Postgraduate Modular Diploma or Degree Course | ||||||||||||||||
| MQF LEVEL | 7 | ||||||||||||||||
| ECTS CREDITS | 5 | ||||||||||||||||
| DEPARTMENT | Technology and Entrepreneurship Education | ||||||||||||||||
| DESCRIPTION | This study-unit introduces teachers to Computational Thinking 2.0 (CT 2.0), extending traditional CT by incorporating AI-driven, data-centric learning methodologies (Tedre et al., 2021). Grounded in a Constructionist approach, the study-unit emphasises learning by doing鈥攅ncouraging children to create, train, and experiment with AI models rather than passively learning about AI. Through hands-on engagement with age appropriate tools such Teachable Machine, QuickDraw, and Scratch AI extensions, teachers will explore how to design activities where students develop, test, and refine their own AI projects, supporting deep learning through exploration and iteration. The study-unit also critically examines ethical and pedagogical considerations, helping teachers navigate topics such as bias in AI models, transparency, and the societal implications of AI. By the end of this study-unit, teachers will be equipped with practical strategies to integrate AI into computational thinking education, fostering an active, inquiry-based learning environment where students construct their own knowledge about AI. Study-Unit Aims: - Bridge the gap between traditional computational thinking and artificial intelligence (AI) by introducing Computational Thinking 2.0 (CT 2.0) principles, including data-driven learning and adaptive problem-solving in primary education; - Adopt a Constructionist approach, enabling children to learn through creation, experimentation, and iteration, using hands-on projects that incorporate AI models; - Equip teachers with practical strategies for integrating AI into primary education through accessible tools such as Teachable Machine, QuickDraw, and Scratch AI extensions; - Develop critical thinking and ethical awareness by exploring AI鈥檚 strengths, limitations, and biases, fostering responsible AI literacy for young learners; - Enhance teachers鈥� confidence in designing pupil-centred, interactive, and AI-enhanced learning experiences that promote creativity, collaboration, and problem-solving. Learning Outcomes: 1. Knowledge & Understanding: By the end of the study-unit the student will be able to: - Explain the core principles of Computational Thinking 2.0 (CT 2.0) and distinguish between AI-driven learning and traditional step-by-step programming approaches; - Evaluate AI as a Constructionist tool, demonstrating how pupils can actively create, experiment with, and refine AI models rather than passively consume AI applications; - Analyse the role of AI-powered computational thinking in supporting student learning, with a focus on pattern recognition, probabilistic reasoning, and interactive exploration; - Critically examine ethical considerations surrounding AI in education鈥攊ncluding bias, fairness, and transparency鈥攁nd develop strategies to introduce these concepts in primary classrooms. 2. Skills: By the end of the study-unit the student will be able to: - Design and implement hands-on AI learning experiences where pupils construct, test, and refine AI models using tools such as Teachable Machine and Scratch AI, fostering an active and inquiry-driven approach to learning; - Facilitate exploratory, project-based AI learning, guiding pupils through interactive and inquiry-led activities that move beyond theoretical instruction to practical application and experimentation; - Adapt AI teaching strategies to cater for different age groups and diverse learning needs, ensuring that AI concepts are introduced in a developmentally appropriate, accessible, and engaging manner; - Promote ethical AI literacy, enabling pupils to critically analyse AI outputs, identify potential biases, and reflect on AI鈥檚 role in society, fostering responsible and informed digital citizenship. Main Text/s and any supplementary readings: Main Texts: - Sentance, S., Barendsen, E., Howard, N. R., & Schulte, C. (Eds.). (2023). Computer science education: Perspectives on teaching and learning in school. Bloomsbury Publishing. - Tedre, M., Denning, P. J., & Toivonen, T. (2021). CT 2.0: Computational Thinking for the AI Era. Koli Calling 鈥�21. |
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| STUDY-UNIT TYPE | Online Learning | ||||||||||||||||
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| LECTURER/S | Leonard Busuttil |
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The University makes every effort to ensure that the published Courses Plans, Programmes of Study and Study-Unit information are complete and up-to-date at the time of publication. The University reserves the right to make changes in case errors are detected after publication.
The availability of optional units may be subject to timetabling constraints. Units not attracting a sufficient number of registrations may be withdrawn without notice. It should be noted that all the information in the description above applies to study-units available during the academic year 2025/6. It may be subject to change in subsequent years. |
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