“Support becomes meaningful when it aligns with someone’s reality”
1. Could you introduce yourself to our readers and talk about your professional journey as a Trainer/Assessor at the School of Allied Health and Human Services, IHNA Australia?
I’m Gaye Witney, RN and Trainer and Assessor at the Institute of Health and Nursing Australia’s School of Allied Health and Human Services. My role spans online, blended, and workplace delivery, where I design learning and conduct competency-based assessment, validation, and moderation, support students’ LLN needs, and liaise with industry on placements—core features reflected across IHNA’s public trainer/assessor role information.
Working within a registered training organisation sharpens my focus on quality and compliance while keeping learning human-centred and practice-oriented. That balance—assuring standards while enabling learners to thrive—has shaped my approach to curriculum and assessment.
2. Your work involves learning and teaching in the context of healthcare education. What inspired you to work in the field of training and assessment in the context of allied health and human services?
I moved into nursing education initially, because it is a direct lever for safer, person-centred care. Now, by equipping Assistants in Nursing with the knowledge, judgement, and communication skills they need, we influence patient safety and dignity across services. I align the course as a “feeder” into the nursing program.
Australian professional expectations—such as the AHPRA Codes of Conduct and the NMBA Registered Nurse Standards for Practice—promote respectful, culturally safe care, confidentiality, minimising risk, and ongoing professional development; I translate those expectations into explicit learning outcomes and assessment criteria (1). Another motivation is the diversity of our learners— many are mature-age, multicultural, multilingual, or career changers. Creating inclusive pathways that embed cultural safety, reflection, and evidence use is both necessary and rewarding, and aligns with national professional guidance.
3. Over the years, what has been the impact of your experience at IHNA Australia on your perspective on contemporary healthcare education, particularly in terms of digital learning and technology?
IHNA’s blended and workplace-integrated delivery has encouraged me to ensure online learning remains authentic and transfers safely to practice. More broadly, Australia’s digital health direction expects a workforce that is digitally capable. The Digital Health Blueprint & Action Plan (2023–2033) (2) sets a ten-year vision for a connected, person-centred health system underpinned by trusted use of data and digital tools, while the Australian Digital Health Capability Framework (3) specifies the capabilities all health workers need—privacy and security, documentation quality, appropriate use of digital tools, and data literacy. These documents have reframed digital competence as a core clinical capability rather than an optional extra.
Generative AI has accelerated the need to rethink assessments. The Australian Skills Quality Authority (ASQA), Australia’s vocational education regulator, is committed to ensuring that artificial intelligence (AI) enhances the effectiveness and quality of our VET education while maintaining the highest standards of ethics, safety and public trust with a growing promotion of assessment reform: assuring learning across programs with multiple, secure points of evidence and, at unit level, including at least one secure task where students must independently demonstrate key outcomes (4) That guidance has strongly influenced how I design assessment blueprints, triangulate evidence, and incorporate observed or oral demonstrations.
4. As an educator working with healthcare students, what are the important aspects of your teaching philosophy that guide you in preparing learners to meet the demands of the emerging healthcare workforce?
My philosophy blends five pillars, with critical thinking, evidence-based practice (EBP), and reflective practice explicitly built into assessment (not only instruction):
Person-centred, culturally safe care is the anchor. We model respectful communication, shared decision-making, and cultural safety—mirroring AHPRA’s and NMBA’s expectations. Assessment rubrics include indicators for therapeutic communication, cultural safety insights, and safe professional documentation (5).
Critical thinking as a professional habit. Students are assessed on their ability to analyse cues, identify risks, evaluate alternatives, and justify decisions. This aligns to NMBA Standard 1 (“Thinks critically and analyses nursing practice”) and is evidenced through decisiontrails, oral defences, and scenario-based questioning (6).
Evidence-based practice as a graded outcome. We teach EBP as a cycle (ask, acquire, appraise, apply, assess). Assessments require learners to cite current guidelines, appraise quality, and explicitly link interventions to evidence—meeting expectations for safe, contemporary practice (7).
Reflective practice as an assessable capability. Guided models (e.g., Gibbs/Driscoll) are used in graded reflections, where students identify what happened, what went well/poorly, and what they would change, with attention to cultural safety and documentation. Reflection and ongoing capability are themes in professional conduct expectations (8).
Higher-order thinking via Bloom’s revised taxonomy. Tasks intentionally target analyse/evaluate/create (e.g., plan justification, error analysis, improvement proposals) rather than recall. This progression is embedded in marking criteria to ensure depth of reasoning is assessed consistently (9).
5. With the emergence of generative AI in higher education, what do you think are the most important aspects of generative AI in terms of its contribution to teaching, learning, and assessment in the context of healthcare and technology?
Used responsibly, generative AI can amplify learning and assessment:
Personalised formative support. AI can help students rehearse rationales, structure notes, and generate practice questions—provided outputs are verified for accuracy and bias and used to stimulate, not supplant, reasoning. Reviews in nursing/health education note potential in tutoring, simulation, and documentation support alongside clear risks (10).
Richer simulation and rehearsal. Virtual “patients” enable safe practice in communication, de-escalation, or dementiafriendly language, followed by assessed reflection and rationale. Literature highlights virtual patient and decisiontutor potential as promising (11).
Documentation efficiency (with verification). Drafting or summarising can save time; assessment then requires students to audit AI output for clinical accuracy, privacy, and plain language—thereby testing documentation literacy and critical appraisal (12).
Assessment design shifts from trying to be “AI-proof” to becoming AI-attuned: requiring process evidence (decisiontrails, search/appraisal logs), combining AI-supported artefacts with oral/observed demonstrations, and including secure tasks where learners must independently perform critical elements (13).
6. What steps can be taken to ensure that generative AI is used in an ethical and transparent fashion in the context of learning and teaching in higher education?
Three policy anchors guide my practice:
TEQSA’s GenAI resources: emphasise systemic assessment redesign, integrity via program-coherent evidence, and realistic responses to limited detectability (14).
UNESCO’s 2023 Guidance: advocates a human-centred approach, transparency, validation of tools for educational use, and capacity-building for teachers and students (15).
EU AI Act (global context): adopts risk-based regulation; for limited-risk systems like chatbots/deepfakes, transparency obligations require that users know they are interacting with AI, and certain emotion-inference uses in education are restricted. Even outside the EU, this provides a useful ethical benchmark (16).
Practical steps I operationalise:
Disclosure by default—students declare what was AI-assisted, how it was used, and how they verified it (transparency principles) (17).
Data protection—prohibit entering personal/health data into public tools; emphasise confidentiality and secure documentation, consistent with professional codes (18).
Human oversight and bias checks—verification of AI outputs becomes an assessed skill (accuracy, bias, clinical fit), echoing responsible-use analyses (19).
Assessment reform—adopt TEQSA pathways (programlevel assurance + unit-level secure tasks) to evidence independent capability (20).
7. As an educator in the context of healthcare education, what are the important aspects that can be taken care of while using AI learning tools to avoid undermining the independent thinking and decision-making abilities of the learners?
To prevent over-reliance on AI, my assessments include: Critical thinking indicators: analysis of cues, risk identification, comparison of options, and defensible rationale. (Aligned with NMBA Standard 1.) (21)
EBP indicators: correct sourcing, appraisal, and application of evidence to the scenario (22).
Reflective practice indicators: self-evaluation, learning needs, cultural safety insights, and documentation quality (23).
8. From your experience as a trainer and assessor, have there been instances of the positive use of generative AI in healthcare education that can be highlighted?
Virtual patient interviews for communication
Students role-play with AI “patients,” then complete an assessed reflective analysis linking choices to evidence and standards. Literature points to virtual patient and tutoring use as promising areas when supervised (24).
Documentation drills. AI produces a first-draft note; students must audit and correct it, citing standards and reducing risk—assessing appraisal, safety, and clear communication in records (25).
Targeted formative feedback. AI generates case variations or practice questions; educators curate for accuracy and alignment, enabling focused practice before summative tasks (26).
9. What are the important aspects that can be taken care of to prepare learners in the context of healthcare to work with digital technologies and AI in the context of healthcare?
I map learning outcomes to national frameworks and emerging global competencies:
Digital health foundations—documentation quality, privacy/security, safe tool use, basic data literacy—aligned to the Australian Digital Health Capability Framework and workforce capability initiatives (27).
AI literacy for clinicians—understanding limitations, bias, and transparency; documenting AI involvement; and communicating uncertainty with patients, echoing proposed clinical competency domains for generative AI use (28).
Safety & governance—human oversight, escalation, incident reporting, and ethical reflection, consistent with UNESCO’s human-centred principles (29).
Professional standards context—confidentiality, cultural safety, reflective practice, and accurate records embedded in every digital/AI activity. (30)
10. What policies and support structures do you think are necessary to support educators in the context of using generative AI in healthcare and technology education?
At provider level, we need:
A clear AI use policy with student-facing guidance, disclosure templates, and examples of AI-appropriate tasks—grounded in TEQSA’s good-practice resources (31). A multi-year assessment reform plan choosing a TEQSA pathway (program-wide coherence; unit-level secure tasks; or hybrid), with resourcing for simulation, OSCEs, and oral defences (32).
Staff capability-building—CPD on prompt design, bias checks, rubric redesign, and educational validation of tools before classroom use (UNESCO) (33).
Data/privacy guardrails—approved tools list, DPIAs, SOPs for student data, and incident reporting, taking cues from UNESCO and the EU’s transparency/risk framing (34).
At system level, aligning curricula to the Digital Health Blueprint & Action Plan (2023–2033) and the Digital Health Capability Framework ensures graduates are genuinely workforce-ready for an increasingly connected, data-driven health system (35).
Conclusion
Generative AI will not replace what matters most in healthcare education: human judgement, empathy, cultural safety, and accountability. But it is changing how we design learning and how we assess readiness for practice. By embedding critical thinking, evidence-based practice, and reflective practice into assessment—alongside transparent AI use, human oversight, and secure demonstrations of independence—we can graduate clinicians who work with AI safely and ethically, always in the service of the person in care (36).