ETEC 533: Technology in the Mathematics and Science Classroom

Description

The future of STEM education will not be shaped by technology alone, but by educators who deliberately decide how technology can support student learning.

The point of AI in education is not to outsource thinking, but to amplify human learning, creativity, and judgement.

— inspired by Ethan Mollick (2024)

Today’s STEM teachers are navigating a world transformed not only by artificial intelligence (AI), but also by rapidly evolving digital tools, simulations, smartphones, data technologies, online platforms, and new forms of human-technology interactions. In this environment, the role of the teacher has become more—not less—important. Teachers are no longer simply users of technology; they are leaders, designers, mentors, and critical decision-makers who help students learn how to think, inquire, create, collaborate, and make sense of increasingly complex technological worlds.

This course explores how educational technologies can meaningfully support STEM learning while keeping pedagogy, disciplinary understanding, and human thinking at the centre. The course will help you find your own answer to this question: How can educational technologies meaningfully support STEM learning without overshadowing pedagogy, disciplinary understanding, and human thinking?

Rather than treating technology as a solution in itself, the course examines how teachers can deliberately and critically integrate technologies to engage learners, deepen conceptual understanding, support inquiry and modelling, and help students use technology for learning rather than distraction or passive consumption (Saville et al., 2024).

Grounded in contemporary STEM learning theories and the Deliberate Pedagogical Thinking with Technology (DPTwT) framework (Milner-Bolotin, 2020, 2025), the course investigates both established and emerging technologies, including simulations, smartphone-based investigations, modelling environments, data collection tools, and artificial intelligence (Milner-Bolotin & Milner, 2023a, 2023b, 2024; PhET Research Team, 2024). Special attention is given to the opportunities and challenges of AI in STEM education and to the growing need for educators who can thoughtfully shape—not simply react to—the future of technology-enhanced learning.

Designed for practicing teachers and graduate students in STEM education, the course combines theory, critical reflection, collaborative inquiry, and practical applications to help educators become informed and confident leaders in a rapidly changing educational world.

Rather than beginning with tools, the course deliberately begins with learning (Bransford et al., 2002; Donovan & Bransford, 2005)

Learning Objectives

By the end of this course, you will be able to:

  • Analyze contemporary issues in Science, Technology, Engineering and Mathematics (STEM) education using peer reviewed research, available online sources, class discussions, personal teaching and learning experiences.
  • Examine how contemporary digital tools, such as educational smartphones apps, computer simulations, data collection and analysis tools, and generative AI, can shape STEM teaching, learning, and assessment in authentic educational contexts.
  • Design and evaluate technology-enhanced pedagogical approaches for mathematics and science learning that are relevant to your own teaching.
  • Assess the implications of modern digital technologies and AI tools for students, teachers, curriculum, and educational systems at large.
  • Reflect critically on your own learning, technology use, and professional growth throughout the course; considering the implications for your own teaching.
  • Connect course readings and experiences to personal inquiries, classroom practice, and broader STEM education contexts.
  • Contribute to the STEM education classroom community through active course participation and support for your peers.

Activities

This course does not assume advanced technical expertise. Instead, it emphasizes critical thinking, pedagogical judgement, creativity, and meaningful STEM learning. Course activities will include:

  • Weekly discussions and collaborative inquiry
  • Research-informed exploration of STEM pedagogies
  • Exploration of practical classroom applications
  • Critical examination of emerging technologies
  • Opportunities to connect theory, practice, and innovation
  • A focus on authentic problems faced by practicing STEM teachers

Course Structure

Part I — Learning First: Theoretical Foundations of STEM Learning

The course begins by examining how students learn STEM subjects and why many learners struggle with conceptual understanding, modelling, problem solving, and transfer of knowledge (Donovan & Bransford, 2005). We will also discuss what STEM education is what it is not and why it matters to us more today than ever before (Martinovic & Milner-Bolotin, 2022).

Course topics may include:

  • Theoretical foundations of STEM learning
  • Constructivism, inquiry, modelling, and collaborative learning
  • Student misconceptions and conceptual challenges
  • Motivation, engagement, and creativity in STEM
  • Learning in the 21st century
  • The DPTwT framework and pedagogy-first technology integration

Part II — Educational Technologies for STEM Learning

In this section, students will explore how specific educational technologies can help address authentic classroom challenges in STEM education. The emphasis will be on purposeful technology integration rather than technology for its own sake.

Possible tools and approaches including, but not limited to:

  • PhET simulations
  • Phyphox and smartphone-based investigations
  • Desmos and visualization tools
  • Data collection and analysis technologies
  • Interactive modelling environments
  • Collaborative and inquiry-based digital tools

Students will critically evaluate how these technologies support conceptual understanding, inquiry, modelling, experimentation, and assessment. The challenges and drawbacks of using technologies will be also discussed. We will also discuss how communicate with the parents and involve them with their children’s technology-enhanced learning.

Part III — Artificial Intelligence in STEM Education

The third section focuses specifically on AI and its emerging role in STEM education (Abdelaal & Kayyali, 2026; Bauer et al., 2025; Cotton et al., 2025; Levin, 2025; Magic School, 2026). AI will be examined separately from other educational technologies in order to critically explore both its pedagogical potential and its risks. We will also focus on the evolving roles of teachers in the age of AI. We will explore how AI can help educators in their work (Magic School, 2026), but also how it might pose significant challenges and allow students to outsource their thinking.

Course topics may include:

  • Generative AI and STEM learning
  • AI-supported inquiry and modelling
  • Co-intelligence and human–AI collaboration
  • Ethical and pedagogical implications of AI
  • AI and assessment
  • Supporting learning versus outsourcing thinking

Readings will include contemporary scholarship on AI in education, including ideas inspired by Ethan Mollick and related work on human–AI collaboration.

Part IV – Final Course Reflection

At the conclusion of the course, students will prepare an individual reflective paper synthesizing their professional learning, evolving perspectives on educational technologies, and implications for their own STEM teaching practice.

Selected Resources

  • Abdelaal, R. M. S., & Kayyali, M. (Eds.). (2026). AI-powered pedagogy, academic transformation, and faculty empowerment. IGI Global. https://doi.org/10.4018/979-8-3373-9220-2.
  • Bauer, E., Greiff, S., Graesser, A. C., Scheiter, K., & Sailer, M. (2025). Looking beyond the hype: Understanding the effects of AI on learning. Educational Psychology Review, 37(2), 45. https://doi.org/10.1007/s10648-025-10020-8
  • Bransford, J. D., Brown, A. L., & Cocking, R. R. (2002). How people learn: Brain, mind, experience, and school. The National Academies Press.
  • Cotton, D. R. E., Wyness, L., Jane, B., & Cotton, P. A. (2025). Redefining assessments in the age of AI. In J. R. Corbeil & M. E. Corbeil (Eds.), Teaching and learning in the age of generative AI (pp. 283-308). Routledge. https://doi.org/10.4324/9781032688602-18
  • Donovan, M. S., & Bransford, J. D. (Eds.). (2005). How students learn: History, mathematics, and science in the classroom. Division of Behavioral and Social Sciences and Education, The National Academies Press.
  • Levin, I. (2025). Papert’s vision realized: Constructionism and generative AI. Constructionism 2025; Building communities, bridging ideas, Zurich, Switzerland.
  • Magic School. (2026). Inside a K–12 AI integration that worked for every teacher. https://www.magicschool.ai/case-studies/west-vancouver-schools
  • Martinovic, D., & Milner-Bolotin, M. (2022). Problematizing STEM: What it is, what it is not, and why it matters. In C. Michelsen, A. Beckmann, V. Freiman, U. Thomas Jankvist, & A. Savard (Eds.), 15 years of MACAS (Mathematics and its connections to the arts and sciences) (pp. 135-162). Springer Nature. https://doi.org/https://link.springer.com/chapter/10.1007/978-3-031-10518-0_8
  • Milner-Bolotin, M. (2020). Deliberate pedagogical thinking with technology in STEM Teacher education. In Y. Ben-David Kolikant, D. Martinovic, & M. Milner-Bolotin (Eds.), STEM teachers and teaching in the era of change: Professional expectations and advancement in 21st Century Schools (pp. 201-219). Springer. https://doi.org/https://doi.org/10.1007/978-3-030-29396-3
  • Milner-Bolotin, M. (2025). From TV to AI: Evolving challenges and enduring questions in STEM Education [Commentary]. Canadian Journal of Science Mathematics and Technology Education, 1-10. https://doi.org/https://doi.org/10.1007/s42330-025-00404-x
  • Milner-Bolotin, M., & Milner, V. (2023a, February 2nd). Breaking the vicious circle of physics disengagement: From undergraduate physics teaching to teacher education [Online webinar]. 81st International Scientific Conference of the University of Latvia 2023: Physics. Education, Practice. Seminar for University Physics Education Practitioners, Riga, Latvia.
  • Milner-Bolotin, M., & Milner, V. (2023b). Smartphone applications as a catalyst for active learning in chemistry: Investigating the ideal gas law. In Y. J. Dori, C. Ngai, & G. Szteinberg (Eds.), Digital learning and teaching in chemistry (pp. 266-280). Royal Society of Chemistry.
  • Milner-Bolotin, M., & Milner, V. (2024). Increasing student science, technology, engineering and mathematics engagement through phyphox activities: Three practical examples. Future in Education Research, 3(3), 111-126. https://doi.org/https://doi.org/10.1002/fer3.50
  • Mollick, E. (2024). Co-intelligence: Living and working with AI (1st ed.). Penguin Random House. https://doi.org/9780593716717
  • PhET Research Team. (2024). PhET interactive simulations. University of Colorado at Boulder. https://phet.colorado.edu/
  • Saville, E., Milner-Bolotin, M., & Anderson, D. (2024). Empowering teachers through an online asynchronous science education cohort graduate program. Journal of Science Teacher Education, 1-19. https://doi.org/10.1080/1046560X.2024.2404798

Assignments

  • Assignment A: You will work in small groups (2–3 students) to investigate and present a selected issue related to STEM learning and teaching.
  • Assignment B: Working in groups, you will design and present a technology-supported STEM learning solution addressing a real classroom problem. The project will include a recorded video presentation and supporting instructional materials, as well as a discussion.
  • Assignment C: You will write an individual scholarly paper examining a specific application of AI in STEM education, with a particular focus on how AI can support meaningful learning rather than replace disciplinary thinking. You will be asked to reflect on how the AI tools you chose might apply to your own teaching practice. You will also be asked to address the challenges posed by overusing or incorrectly using this AI tool.
  • Assignment D: You will write an individual final reflection discussing your own learning, how your thinking was informed by the literature, discussions and presentations in the course.

 

Minor course topic, activity, reading/resource, and assignment details may change from year to year.