Agentic education · Learning science

Actionable companion · Retention & transfer

Learning with agency: Exploring the Opportunities and Challenges of Healthcare Innovation in UK Higher Education: A Narrative Review

AI-assisted editorial implementation guide, separate from the original research. Proposed actions are not claims or recommendations attributed to the source authors. No independent peer review of this guide is asserted.

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Evidence and context

Title and abstract screening; stage labels describe reported settings, not verified numerical ages. Design labels require full-text appraisal. Reported stage(s): Higher education. Verify the participant population and task before transferring the findings.

Original authors’ abstract, reproduced under the linked source license:

Abstract Background: The healthcare sector is under increasing pressure due to an ageing population, rising multimorbidity, and a projected global workforce shortage of 10 million by 2030. It is becoming increasingly apparent that addressing these challenges requires more than simply increasing workforce numbers—it necessitates a shift towards innovative practices in healthcare education. Higher education (HE) plays a crucial role in preparing future healthcare professionals, yet embedding innovation within HE presents challenges such as resistance to change, resource limitations, and difficulties in interdisciplinary collaboration. This review explores the barriers and opportunities associated with fostering innovation in HE health programmes. Methods: This narrative review explored the recent literature on innovation in healthcare HE, examining national policies, institutional strategies, and emerging technological advancements. It describes the impact of digital learning tools, simulation-based education, artificial intelligence (AI), and interprofessional education (IPE) on student learning outcomes and workforce preparedness. Results: Findings suggest that while digitalisation and AI-driven innovations offer opportunities to enhance HE health programmes, their effectiveness is dependent on appropriate pedagogical integration and resource. Challenges include upskilling workforce to new more modern methods, ensuring equitable access to digital resources, and maintaining a balance between technological innovation and traditional face-to-face learning. Conclusions: To embed innovation effectively within healthcare HE, institutions may need to prioritise interdisciplinary collaboration, entrepreneurial thinking, and sustainable funding models. While technology is key to modernising education, it should be implemented alongside evidence-based pedagogical strategies to ensure meaningful learning outcomes and long-term workforce preparedness. Keywords: healthcare, innovation, technology transfer, nursing education, higher education, health information technology

What kind of evidence is this?

Supporting AI learning science · Evidence synthesis

Stage: Higher education. Screening labels describe reported context and study-design language. They are not a risk-of-bias rating or proof of effectiveness for an age band.

Study methods · source passage

To address this complex topic, a narrative review of the recent literature was conducted to explore the challenges and opportunities associated with implementing innovation in HE. The search process was conducted in October 2023 using PUBMED. A search was performed using a Boolean search strategy, combining the keywords “Barriers” AND “Innovation” AND “Higher Education”, along with relevant synonyms. Given the complexity of the topic, the inclusion criteria were peer-reviewed publications from 2020 onward, available in German, Dutch, or English, with full-text accessibility, and relevant to the fields of healthcare and education. To identify additional relevant studies, a hand search was conducted to locate materials not retrieved through the systematic search.

Limitations and uncertainty · source passage

This discussion resonates with the introduction’s emphasis on the challenges of implementing innovative practices in educational settings. The concept of learning by doing, rooted in the work of early educational progressives like John Dewey, posits that students learn most effectively through hands-on, real-world experiences [24]. Despite the abundance of online educational resources, the absence of bedside teaching in health-related programmes has negatively impacted students’ direct interaction with patients, compromising the development of physical examination and non-technical skills [25]. Papapanou et al. also highlight this issue, arguing that the limitation of interpersonal contact in e-learning hinders the development of essential communication and empathy skills needed for patient interaction [22]. Another challenge in e-learning is the potential for ethical issues, such as unequal access to reliable internet, which can exacerbate educational disparities [20]. Despite these challenges, the importance of preparing students in health science HE for a digital work environment remains paramount, aligning with the broader aim of fostering innovation in HE health programmes.

Design the system before the pilot

Use this study to inform a learning mechanism or implementation question. It is not direct evidence that autonomous agents improve learning. Keep the proposed agent behavior separate from what the source actually tested.

  • Include a delayed assessment and an unfamiliar transfer problem without AI. Immediate supported performance cannot establish durable learning.

A 90-day district pilot

  1. Days 1–15 · Appraise. Have the curriculum lead and a teacher read the methods and limitations. Record the study design, sample, comparator, outcomes and evidence gaps. Define a local question that the study can help inform.
  2. Days 16–30 · Design. Choose one disciplinary task. Require a first attempt, a documented AI consultation, a critique of the output and an independent explanation. Align the comparison group and scoring rubric with the same objective. Name an accountable owner and set the baseline, success threshold and budget before implementation.
  3. Days 31–60 · Test. Start with a small voluntary cohort, preserve an accessible non-AI route and require teacher review. Use synthetic or de-identified examples; approve data handling and tool terms through district processes.
  4. Days 61–90 · Decide. Blind-score reasoning and accuracy on a new task without AI. Distinguish model examination scores, student perceptions and actual learner gains; only the last directly tests educational benefit. Compare with baseline, document uncertainty and stop or revise if learning worsens, access is unequal or critical errors persist.

The board decision brief

Report the learning problem, evidence quality, participating population, total cost including review time, measured benefit, errors, access differences and remaining uncertainties. Make a dated recommendation to stop, revise or expand. A positive perception survey alone does not establish learning improvement.

Attribution and reuse

Renske Emicke; Ashley Shepherd; Dylan Powell. Exploring the Opportunities and Challenges of Healthcare Innovation in UK Higher Education: A Narrative Review. Nursing Reports (2025-05-14). https://doi.org/10.3390/nursrep15050171

Companion prepared 2026-10-30. Original authorship and DOI are preserved. The abstract is an attributed source extract; the implementation plan is new AI-assisted editorial material, offered under the same CC BY 4.0 license. This guide has not undergone independent peer review.