Technology‑enhanced learning strategies (TELS) are now routinely incorporated into higher education curricula. In biomedical and health sciences, students use a wide range of tools, from learning management systems, discipline platforms and simulations, lecture capture, peer discussion spaces. This expands to a suite of student‑selected resources such as Anki, YouTube, and generative artificial intelligence (GenAI).

While these technologies are often introduced to improve student engagement, an important question remains: do they actually increase students' motivation and interest to learn?

This study forms part of a broader collaborative project between Macquarie University and The University of Sydney investigating the effects of TELS on students’ motivation, engagement, interest, and academic self‑efficacy. Here, we report discipline‑specific insights into the relationships between technology, autonomy, and interest development in biomedical and health sciences education. The pattern emerging from our mixed‑methods evidence is as follows: Technology + Autonomy + Interest = Motivation + Engagement. Let’s explore this pattern and how it can be utilised in an educational setting.

Looking Beyond Technology

Our design was grounded in established motivation and interest theories, including Self‑Determination Theory (Ryan & Deci, 2000), Reiss’ Theory of 16 Basic Desires (2004), and the Four‑Phase Model of Interest Development (Hidi & Renninger, 2006). We combined questionnaire data and student interviews to explore how different technologies influenced motivation, interest, and learning experiences.

Qualitative prompts probed not simply whether interest shifted, but what participants attributed those shifts to and how technologies were positioned within their broader learning strategies.

Finding 1: Technology Supports Interest Rather Than Creates It

End‑of‑semester interest was systematically higher in subjects that students identified as already interesting at the outset. When participants explained increases in interest, the dominant drivers were pedagogical and epistemic rather than technological: educator engagement, subject matter, practical learning experiences, career relevance, and peer interaction. TELS were rarely positioned as a primary generator of interest.

For experienced educators, the implication is not that technology is inert, but that its motivational function is more reliably instrumental and sustaining than originating. Technologies appeared to help students maintain and build on existing interest rather than create interest from scratch; especially where instructional design already creates coherence, relevance, and challenge.

Notably, we observed a clear positive association between interest and intrinsic motivation: where interest strengthened, intrinsic motivation tended to rise. This reinforces interest as a key proximal construct for engagement and persistence and suggests that technology initiatives should be evaluated partly on their capacity to support interest development, not merely on usage metrics.

Finding 2: Choice Matters More Than the Tool

The most robust differentiator in our dataset was autonomy. Students reported feeling more motivated and seeing greater value in tools they chose for themselves than in tools prescribed by their institution. Participants consistently articulated autonomy as the capacity to decide:

• whether to use a tool at all
• which tool to use
• how to configure and integrate it into their workflow
• when to deploy it across the study cycle

Importantly, students did not uniformly evaluate university-led tools negatively. Rather, motivational friction emerged when tools were experienced as compulsory, misaligned with personal study practices, or constraining in workflow. Conversely, self‑selected tools were often valued for low onboarding cost, efficiency gains, and configurability.

From a Self-Determination Theory lens, this is unsurprising: autonomy support is a primary driver of internalisation and intrinsic motivation. The practical translation is that the pedagogical question is not simply “Which platform is best?” but “What conditions of autonomy are we creating around the platform?” Even high‑quality institutional tools may underperform motivationally if implemented in ways that reduce perceived agency.

A pragmatic observation also emerged: competence with effective tools could make low‑interest subjects more tolerable. While technology may not generate interest, it can reduce friction, support persistence, and enable goal‑directed study behaviours, particularly when students can choose tools that fit their preferences.

Finding 3: Students See Both Promise and Risk in GenAI

Around half of participants reported using GenAI. The qualitative data revealed a bifurcated landscape: some students framed GenAI as a high‑utility explanatory and rehearsal resource (concept clarification, revision prompting, feedback-like interaction, lowered help-seeking barriers), while others highlighted reliability, privacy, over-reliance, and risks to critical thinking.

Students also indicated uneven AI and digital literacy, suggesting that GenAI’s benefits and harms are likely to be distributed inequitably without deliberate capability-building. Many participants were receptive to structured instruction in ethical and effective GenAI use, and some explicitly wanted GenAI embedded into learning activities rather than treated as an academic integrity risk.

For curriculum leaders, this positions GenAI as a design challenge at the intersection of assessment validity, academic integrity, and student agency. The emerging requirement is a calibrated approach: tighter guidance and clearer boundary conditions, while preserving meaningful autonomy in learning processes.

Implications: group TELS by who chooses them, then design for autonomy

Students did not describe a simple “good vs bad” split between educator‑led tools and self‑selected tools. What mattered more was what the tool was for and how much choice students had in using it. A practical way to plan curriculum and guidance is to think about TELS as three categories:

• Educator‑led TELS that are created or customised for the subject
  Examples: the learning management system/subject site structure, tailored learning modules, simulations, discipline platforms, subject-specific chatbots.
  Design focus: keep them coherent, easy to navigate, and clearly aligned to learning outcomes and assessment.
• Educator‑led use of existing tools
  Examples: structured GenAI activities, required or guided use of GenAI for specific learning tasks, explicit GenAI parameters for assessment preparation.
  Design focus: be transparent about purpose, boundaries and limitations, teach responsible/effective use, and build student capability.
• Self‑selected TELS students adopt to support their own study
  Examples: note‑taking and organisation systems, spaced repetition tools (e.g., Anki), YouTube, self-directed GenAI for clarification and revision.
  Design focus: assume tool diversity, and design learning activities and resources that remain effective regardless of what students choose.

Across all three categories, students valued technologies that were easy to use, supported efficient learning, and allowed flexibility in how they were used. Hence, the overarching design principle is the same: provide structure where it helps but preserve meaningful choice so students can exercise autonomy in how they learn.

For experienced practitioners, this aligns with an autonomy‑supportive and Universal Design for Learning (UDL)‑compatible stance: provide structure and high-quality defaults but avoid unnecessary constraint where learners can productively self-regulate.

Conclusion

For biomedical and health sciences education, our evidence suggests that TELS are most motivationally effective when positioned as supportive infrastructure, not as the primary engine of engagement. Students' motivation is shaped primarily by what they are learning and how educators engage them. Technology is most effective when it supports these foundations rather than attempts to replace them. Technology can strengthen motivation, but the mechanism is most reliable when students experience autonomy and when tools sustain (rather than substitute for) interest development.

As universities continue to integrate GenAI and other emerging technologies, the challenge is not simply choosing the right tools. It is creating learning environments that nurture interest, support autonomy, and help students use technology in ways that enhance learning. Our findings suggest that the motivation equation is not technology alone. It is Technology + Autonomy + Interest.

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