ASPIRE: a pedagogical framework for integrating generative artificial intelligence in public health education

Abstract

The rapid integration of generative artificial intelligence (GenAI) in higher education has intensified concerns regarding assessment validity, academic integrity, and educational equity. In response, higher education institutions have mobilised significant resources to address these challenges. However, many responses remain largely discursive, relying on policies or guidance to regulate student behaviour without addressing the underlying design of learning and assessment. Such approaches risk superficial engagement and may exacerbate inequities in GenAI-enabled learning environments. This paper presents ASPIRE, a data-informed and theory-driven pedagogical framework that emerged from the iterative integration and evaluation of GenAI within a postgraduate Epidemiology and Principles of Research unit at the University of Canberra (Australia). Drawing on insights from this evaluative work and grounded in established educational design principles, including constructive alignment and scaffolded learning, ASPIRE conceptualises GenAI as a cognitive and epistemic resource shaped by learning and assessment design. The framework guides educators through a structured design cycle that involves Assessing curriculum and assessment for pedagogically appropriate GenAI integration; Scaffolding students’ AI literacy and ethical engagement; Planning assessments that make students’ reasoning and judgement visible; Implementing learning and assessment with clear expectations and guided support; Reflecting on teaching practice and student learning processes; and Evaluating outcomes to inform ongoing refinement. Developed within public health education, ASPIRE responds to discipline-specific priorities, including ethical decision-making under conditions of uncertainty and evidence-based reasoning. While grounded in this context, the framework is adaptable to other applied disciplines facing similar ethical and assessment challenges. ASPIRE offers educators a coherent approach to integrating GenAI in ways that support meaningful learning and responsible professional preparation.

Introduction

Generative artificial intelligence (GenAI) enables students to complete some assessments without applying required skills, undermining the validity of those assessments (Nikolic et al., 2024). If educators cannot confirm genuine learning, assessments lose their educational value (Dawson et al., 2024). Recent literature highlights that many institutional and educator responses to the challenges posed by GenAI have been largely discursive in nature (Corbin et al., 2025). According to Corbin et al. (2025) discursive changes refer to strategies that rely primarily on communicating expectations, policies, or rules to students, such as AI usage declarations or academic honesty statements, without changing the underlying structure of the assessment task itself. These approaches place full responsibility on students to interpret and comply with guidelines, assuming a level of digital literacy, ethical maturity, and voluntary compliance that cannot be guaranteed. Such measures often create an appearance of rigour without substantive changes to assessment design, leaving assessments fundamentally unchanged and still vulnerable to GenAI misuse (Luo, 2024). Educators are expected to monitor compliance with little practical guidance, and students are rarely given the support or scaffolding needed for appropriate GenAI use. This overreliance on discursive approaches results in uneven responses, inconsistent enforcement, and persistent risks to educational integrity (Coelho et al., 2025). Without structural changes to how learning is supported and assessed, students may “pass” without meaningful engagement, and equity gaps may widen as those with higher AI fluency gain disproportionate advantages (Corbin et al., 2025). This suggests the need to move beyond discursive responses and adopt structural solutions that reflect the realities of GenAI-integrated learning (Williams, 2025). Achieving this shift requires practical and evidence-informed frameworks that guide educators in integrating GenAI meaningfully into both teaching and assessment (Nikolic et al., 2024).

There are emerging frameworks that guide GenAI integration in education, most notably the PAIR (Acar, 2023), EThICAL (Francis et al., 2024), and IDEE (Su and Yang 2023) frameworks. These models provide much-needed structure as educators begin to respond to the increasing presence of GenAI tools in learning environments. The PAIR framework (Problem, AI, Interaction, Reflection) offers a pedagogically grounded model that encourages students to define problems, engage with AI tools, and reflect on their learning process (Acar, 2023). The EThICAL framework emphasises ethical considerations, urging educators and students to examine policies, consider social impact, critically engage with outputs, and use secure, transparent AI tools (Francis et al., 2024). This model aligns well with the values of integrity and accountability, particularly in disciplines handling sensitive data where privacy is critical, such as Epidemiology. The IDEE framework (Identify, Demonstrate, Evaluate, Enhance) adds a useful emphasis on aligning GenAI use with learning outcomes (Su and Yang 2023). It supports reflective practice by encouraging educators to assess and refine their AI-enhanced teaching strategies.

Despite these contributions, a common limitation that is becoming increasingly evident is that most emerging frameworks remain largely theoretical or conceptual, with limited grounding in empirical research or implementation data (Zhang et al., 2024). Their applicability, effectiveness, and real-world benefits remain largely untested, which raises questions about their practical utility across diverse learning environments. Many of these frameworks fall short in addressing the complexities of student engagement, discipline-specific requirements, and the realities of teaching with GenAI (Qu et al., 2024). As Corbin et al. (2025) argue, effective responses require structural, not merely discursive changes, with assessment playing a central role. Yet, most frameworks offer little support for developing assessment tasks that uphold academic integrity while fostering GenAI literacy (Bittle and El-Gayar, 2025), which the World Health Organization (WHO) has identified as core competency for the next generation of health care professionals (WHO, 2023).

This gap is especially significant in public health education, where assessments often require applied reasoning, ethical judgement, and engagement with complex real-world problems (Wang and Li, 2024). Accordingly, building on our integration of GenAI within a postgraduate Epidemiology and Principles of Research unit at the University of Canberra (Mengesha et al., 2025), this paper presents the ASPIRE framework as a pedagogical contribution to supporting the holistic integration of GenAI into public health education, with potential relevance to other disciplines facing similar ethical and assessment challenges. In doing so, this work responds to the expanding influence of GenAI across health and medical education and the urgent need for structured, pedagogically grounded approaches to its integration within public health education (Tran et al., 2025).

GenAI integration into public health education at the University of Canberra

Details of the GenAI integration process and its outcomes, including the pedagogical rationale, assessment redesign, implementation, and evaluation results, have been reported elsewhere (Mengesha et al., 2025). What follows is a concise three-step overview of the key elements underpinning the integration. The integration of GenAI into the postgraduate Epidemiology and Principles of Research unit was informed by constructivist learning theories, including Biggs’ constructive alignment (Biggs, 1996) and Vygotsky's social constructivist theory (Vygotsky, 1978). These theories informed the intentional design of GenAI-enabled learning and assessment activities that promote active knowledge construction and critical reflection rather than surface-level content generation. There were two key considerations that informed the integration of GenAI. First, there was a risk that students might use GenAI tools without guidance in completing the summative critical appraisal assessment, which constitutes 40% of the unit's overall grade, potentially breaching academic integrity and compromising the validity of assessment. Second, when used transparently and critically, GenAI offers significant potential to enhance learning. The purposeful integration was therefore designed as a dynamic and iterative process, continuously refined in response to student feedback and emerging insights.

Step 1: Pedagogical alignment

A key consideration in reviewing the alignment between the unit's intended learning outcomes (ILOs) and assessment tasks was to ensure that achievement of the ILOs was not compromised by the integration of GenAI. We confirmed that, through redesign of the critical appraisal task, the assessment continued to evaluate complex cognitive processes, specifically analysis and evaluation, as articulated in Bloom's revised taxonomy (Anderson and Krathwohl, 2001). By intentionally integrating GenAI, we mitigated the risk of inappropriate student use while maintaining the validity of the assessment.

Step 2: Scaffolding GenAI use

To facilitate the ethical and effective integration of GenAI into student learning, we adopted Salmon’s (2012) five-stage model of online learning as a structured framework for pedagogical scaffolding (Salmon, 2012). This began with building access and motivation through a dedicated lecture and tutorial that introduced the conceptual, practical, and ethical dimensions of GenAI. Students explored its relevance in public health, its underlying mechanisms, and limitations such as bias and hallucination, while also learning prompt engineering strategies to enhance meaningful engagement. To promote peer learning and a sense of community, online collaborative tools were used to support peer interaction and reflection, enabling students to share their expectations, experiences, and concerns, and to reflect on prompt strategies through dialogue with their peers (Zainuddin et al., 2020). Information exchange was facilitated through six structured activities using Copilot, where students practiced summarising literature, identifying references, and critically appraising studies. The RTSI model (Role, Task, Structure, Instructions) was introduced to improve prompt writing (Lo, 2023), and students used the Joanna Briggs Institute tool for appraising randomised controlled trials as a guide to verify and interpret GenAI-generated content (Barker et al., 2023). These activities laid the foundation for knowledge construction, as students applied GenAI to support their critical appraisal assessments, integrating outputs with disciplinary knowledge while receiving guidance to avoid overreliance or misinterpretation. Finally, the development stage focused on ethical reflection, with students submitting a structured critical reflection worth 10 percent of their final grade. This reflection task was embedded to promote metacognitive awareness and reinforce the importance of transparency and principled engagement with emerging technologies.

Step 3: Evaluation and results

The integration of GenAI into the unit was evaluated using a mixed-methods approach that combined pre- and post-intervention surveys comprising both quantitative items and open-ended questions. These instruments captured students’ reflections on their overall experience, student perceptions and self-reported capability, the strategies they developed to use Copilot in support of learning and assessment, and any concerns associated with GenAI-mediated tasks. Findings from this evaluation are briefly summarised here to provide context for the pedagogical insights discussed in this framework paper, while full methodological and analytical details are reported elsewhere (Mengesha et al., 2025). Quantitative analysis demonstrated statistically significant improvements in students’ self-reported understanding of GenAI and their ability to use it effectively (n = 78; pre-intervention vs. post-intervention comparison using Fisher's exact test, p < 0.001). Complementing these findings, thematic analysis of the qualitative responses identified five key themes: tensions between automation and authentic learning; prompt literacy as an emerging academic skill; GenAI as a catalyst for metacognitive engagement; ethical ambiguity and cognitive dissonance; and future-oriented learning. These insights underscore the importance of thoughtful pedagogical design when integrating GenAI into curriculum and assessment.

The ASPIRE framework

ASPIRE is a data-informed and theory-driven pedagogical framework that emerged from our iterative integration and evaluation of GenAI within a postgraduate Epidemiology and Principles of Research unit in a public health curriculum. It was designed to support educators in holistically embedding GenAI into public health teaching and assessment. Conceptually, the framework is grounded in established educational design principles, including constructive alignment, scaffolded learning, and sociocultural theories of learning. Rather than positioning GenAI as an external technology to be managed through policy or ethical guidance alone, ASPIRE conceptualises GenAI as a cognitive and epistemic resource whose educational impact is shaped by intentional curriculum and assessment design. The framework comprises six interconnected phases—Assess, Scaffold, Plan, Implement, Reflect, and Evaluate—which operate as a cyclical process that embeds GenAI use structurally within learning activities and assessment practices (Figure 1).

Figure 1

The development of ASPIRE within public health education is theoretically and pedagogically significant. Public health education places strong emphasis on ethical decision-making under conditions of uncertainty, critical appraisal and use of evidence, population-level reasoning, and professional accountability for real-world health consequences (WHO, 2024). These disciplinary characteristics inform how GenAI can be used responsibly and meaningfully and underpin ASPIRE's emphasis on assessment validity, transparency, and reflective judgement. The framework addresses these disciplinary demands by centring assessment practices that require students to justify decisions, evaluate evidence, and reflect on the ethical implications of AI-mediated outputs, thereby aligning GenAI use with the epistemic and professional expectations of public health practice.

Phase 1: Assess possible GenAI integration

From the perspective of constructive alignment (Biggs, 1996), this phase ensures that the intended learning outcomes, learning activities, and assessments continue to align coherently to maintain pedagogical integrity in an educational landscape increasingly shaped by GenAI. Rather than assuming that GenAI should be integrated into all units or courses, the Assess phase encourages educators to critically examine whether, and how, GenAI intersects meaningfully with a unit's learning outcomes and assessment design (Attewell, 2024). At the centre of this phase is a critical review of learning outcomes to determine whether students can still authentically demonstrate the intended capabilities in a context where GenAI tools may be accessible. This involves evaluating whether current assessment tasks accurately measure the skills and knowledge the unit aims to develop, whether they are vulnerable to being undermined by AI-generated content, or whether they are robust enough to retain their integrity regardless of GenAI use (Xia et al., 2024).

In some cases, GenAI may be a legitimate and even valuable part of the learning process, particularly when its use reflects real-world practices in public health research, communication, or data analysis (Holmes and Miao, 2023). In other cases, it may be necessary to redesign assessments to preserve academic integrity or to explicitly restrict GenAI use where it could obscure students’ independent thinking and skill development (Han et al., 2025). If, after this review, the unit is deemed suitable for GenAI integration, either through adaptation or by design, the process moves to the next phase.

Phase 2: Scaffold AI literacy and ethical use

Drawing on social constructivist learning theory (Vygotsky, 1978), this phase of the framework foregrounds GenAI as a mediating tool that students use, with appropriate support, to co-construct their disciplinary, ethical, and professional knowledge. To ensure that students interact with GenAI as intended, this phase also draws on Biggs’ constructive alignment theory, aligning ILOs, learning activities, and assessment tasks so that students are supported to develop both technical competence and critical, ethical judgement in the use of GenAI (Biggs, 1996).

Consistent with these principles, this phase adopts a two-part design approach. First, intentionally designed learning activities are embedded within the curriculum to provide collaborative, meaning-making opportunities through which students externalise their thinking and critically interrogate GenAI outputs in relation to disciplinary knowledge and ethical norms. Second, these activities are designed to cultivate both technical proficiency and critical, ethical awareness in students’ use of GenAI tools (Alghasab, 2025), while engaging with the broader social, ethical, and professional implications of these technologies within public health education and practice (Smith et al., 2026).

To facilitate this, learning experiences should introduce students to ethical considerations associated with GenAI use, including issues of authorship, bias, misinformation, and accountability (Huynh et al., 2025; Quince and Nikolic, (2026), while also addressing digital equity through access to appropriate tools and opportunities to develop digital literacy skills (Palvia et al., 2018). Embedding such learning activities within the curriculum supports students in completing assessments that evidence achievement of the ILOs, while also providing structured opportunities to test, question, and refine GenAI outputs. Further, and as part of this design intent, learning activities and assessments are structured to enable reflection on how GenAI shapes students’ thinking, decision making, and emerging professional identity within public health practice. Integrating these design elements within the curriculum scaffolds students’ AI literacy and cultivates a culture of thoughtful, responsible, and contextually relevant GenAI use in public health education.

Phase 3: Plan AI integrated assessments

In this phase, assessment functions as a key lever for preserving validity, academic integrity, and ethical judgement in the use and interpretation of GenAI outputs. Following the principles of constructive alignment (Biggs, 1996), the focus is on designing assessments that meaningfully incorporate GenAI in ways that reliably measure the ILOs and facilitate the development of students’ ethical judgement while reflecting the realities of professional practice (Chiu, 2024). Where appropriate, GenAI should be embedded within assessment tasks in ways that promote critical engagement rather than passive content generation (Ang, 2025). This requires intentional assessment design that maintains academic rigour by promoting higher-order thinking, including judgement, critique, and analysis.

To operationalise this alignment-focused approach to assessment, a core principle of the ASPIRE framework is the deliberate design of assessments that make students’ thinking visible, positioning assessment as the primary site through which learning processes are evidenced. This can be achieved by embedding task mechanisms that require students to articulate how and why GenAI tools were used, for example through reflective journals, annotated drafts, iterative submissions, or structured peer interaction using online collaborative tools (e.g., Padlet) (Zhan et al., 2025). Equally important is the design or revision of marking rubrics to align with these expectations (Chan and Colloton, 2024). Rubrics should clearly specify how GenAI use is assessed, including criteria related to ethical engagement, critical analysis, and the quality of reflection.

Phase 4: Implement scaffolded learning and assessment

This phase focuses on the implementation of scaffolded learning activities and assessments, bringing the curriculum design into active practice. At this stage, students engage directly with structured learning experiences that integrate GenAI within a clearly defined and ethically grounded framework. Effective implementation requires clarity, transparency, and consistency both in assessment delivery and day-to-day learning activities that prepare students for responsible and reflective use of GenAI.

Educators play a central role in guiding students through these experiences, ensuring they understand how GenAI fits into their learning journey. This includes clearly communicating expectations around GenAI use, whether in formative tasks, collaborative projects, or summative assessments. Instructions should articulate how GenAI may be used, how its use should be documented, and how it connects to learning outcomes and public health competencies. Scaffolded activities such as ethical case studies, reflective exercises, and iterative design tasks should be embedded throughout the learning process, reinforcing both technical skills and ethical awareness.

Assessment implementation should be equally transparent, with marking rubrics that reflect the inclusion of GenAI and its implications. These rubrics should be constructed to evaluate not only the final product, but even more importantly, the process of learning, including students’ ability to critically engage with GenAI outputs, reflect on their use, and demonstrate originality and professional judgment. Throughout this phase, educators foster a supportive learning environment by remaining accessible to clarify expectations, address concerns, and provide formative feedback, thereby enabling students to navigate learning activities and assessments with confidence and to engage with GenAI tools thoughtfully and effectively. This highlights the importance of planning structured learning experiences to develop professional capabilities for public health workers in a digitally evolving world, by integrating scaffolded learning and assessment via a cohesive implementation strategy.

Phase 5: Reflect

The Reflect phase positions reflection as a core pedagogical practice through which educators critically examine their experiences of integrating GenAI into teaching and assessment. Consistent with Xia et al. (2024), reflection is treated as an ongoing component of teaching practice that supports educators’ sense-making of how GenAI integration unfolded in relation to the design intentions established in the Plan phase. Educator reflection focuses on the implementation of GenAI-integrated learning and assessment, including the effectiveness of scaffolding strategies, the clarity of expectations, and the extent to which assessment designs functioned as intended. This phase supports educators in considering how GenAI influenced pedagogical decision-making, assessment practices, and professional judgement, as well as in identifying tensions, uncertainties, or unintended effects that emerged during enactment.

While reflection is centred on educators’ practice, ASPIRE emphasises the deliberate inclusion of student reflection within assessment and curriculum design, planned during the Plan phase, as a source of pedagogical evidence. Drawing on Kolb's reflective learning cycle, structured reflective components embedded within assessment tasks, such as reflective statements or annotated submissions, enable students to articulate changes in confidence, developing professional judgement, and ethical awareness in relation to GenAI use (Kolb, 2014). These reflections make students’ thinking visible in ways that are pedagogically purposeful rather than incidental. Student-generated reflective evidence serves to inform educators’ reflective judgement about the coherence and effectiveness of GenAI-integrated design, without constituting formal evaluation. In this way, the Reflect phase supports professional sense-making and prepares the ground for the more systematic appraisal of outcomes undertaken in the subsequent Evaluate phase.

Phase 6: Evaluate

The final phase positions evaluation as a mechanism for sustaining assessment validity and academic standards as GenAI capabilities evolve. Informed by reflective insights from both educators and students, evaluation within the ASPIRE framework focuses on two key points: the epistemic reliability of assessment as a site for academic integrity, and as a source of credible evidence of student learning. One, from a constructive alignment perspective (Biggs, 1996), educators examine whether assessment evidence continues to support defensible inferences about what students know and can do in GenAI-enabled contexts, and review alignment between learning outcomes, assessment tasks, and standards to ensure validity is maintained over time. Secondly, this phase requires an evaluation of students’ performance outcomes, assessment scores and competency improvements. This ensures that the impact of GenAI integration on student learning is properly scrutinised and pedagogically justified. Pedagogical justification essentially means that GenAI enhances learning in substantive ways and not just functions as a tool for efficiency and that students’ use of GenAI does not lead to unethical conduct. By feeding evaluative insights into earlier phases, this stage positions ASPIRE as an iterative framework, enabling continuous improvement and responsive refinement of curriculum and assessment design.

Conclusions

The ASPIRE framework contributes to the emerging scholarship on GenAI in education by placing assessment validity and learning design at the centre of GenAI integration. While many existing frameworks foreground student–AI interaction, ethical principles, or alignment with learning outcomes, ASPIRE explicitly links GenAI use to how evidence of learning is generated, interpreted, and evaluated. This reframing positions GenAI integration as a structural design challenge rather than a matter of regulating student behaviour, thereby redirecting attention toward assessment practices that sustain academic integrity and educational quality in GenAI-enabled contexts.

ASPIRE supports pedagogically sound and ethically responsible GenAI integration by guiding educators through a coherent design cycle encompassing assessment review, scaffolding of AI literacy, purposeful assessment design, structured implementation, and refinement through reflection and evaluation. Within this cycle, GenAI is conceptualised as a cognitive and pedagogical resource that supports deeper learning and capability development, rather than as a shortcut for task completion. The framework is intentionally flexible, enabling educators to engage with the full cycle or with specific phases according to their goals, institutional contexts, and levels of experience with GenAI. This flexibility allows multiple entry points and supports adaptation across varying levels of GenAI maturity in teaching practice. Through this approach, ASPIRE promotes the development of technical proficiency alongside critical judgement and ethical awareness—capabilities that are increasingly essential for health professionals operating in digitally mediated environments.

Although developed within public health education, ASPIRE has relevance for other disciplines in which assessment validity, ethical reasoning, and professional judgement are central learning outcomes. Fields such as health professional education more broadly, the social sciences, and other applied disciplines may find the framework useful for structurally embedding GenAI into learning and assessment design in ways that foreground reasoning processes and support defensible inferences about student learning. Importantly, ASPIRE is not presented as a fixed or final framework. As educators apply it within public health and adapt it to other disciplinary contexts, there remains scope for ongoing refinement informed by empirical evidence, reflective practice, and shared scholarship. In this way, ASPIRE is positioned as a dynamic, evidence-informed framework that can evolve alongside technological change, supporting educators in sustaining educational quality and preparing graduates to navigate complex, GenAI-enabled professional landscapes.

References

• 1

  AcarO. A. (2023). Are your students ready for AI?: A four-step framework to prepare learners for a ChatGPT World. Harvard Business Publishing Education. Available online at: https://hbsp.harvard.edu/inspiring-minds/are-your-students-ready-for-ai? (Accessed September 09, 2025).

  • Google Scholar

• 2

  AlghasabM. B. (2025). English As A foreign language (efl) secondary school Students’ use of artificial intelligence (ai) tools for developing writing skills: unveiling practices and perceptions. Cogent Educ.12, 2505304. 10.1080/2331186X.2025.2505304

  • CrossRef
  • Google Scholar

• 3

  AndersonL. W.KrathwohlD. R. (2001). A Taxonomy for Learning, Teaching, and Assessing: A Revision of Bloom’s Taxonomy of Educational Objectives: Complete Edition. New York: Addison Wesley Longman, Inc.

  • Google Scholar

• 4

  AngC.-S. (2025). Developing ai literacy in healthcare education: bridging the gap in competency assessment. Discov. Educ.4, 372. 10.1007/s44217-025-00812-z

  • CrossRef
  • Google Scholar

• 5

  AttewellS. (2024). Embracing Generative Ai In Assessments: A Guided Approach.

  • Google Scholar

• 6

  BarkerT. H.StoneJ. C.SearsK.KlugarM.TufanaruC.Leonardi-BeeJ.et al (2023). The revised jbi critical appraisal tool for the assessment of risk of bias for randomized controlled trials. JBI Evid. Synth.21, 494–506. 10.11124/JBIES-22-00430

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 7

  BiggsJ. (1996). Enhancing teaching through constructive alignment. High. Educ.32, 347–364. 10.1007/BF00138871

  • CrossRef
  • Google Scholar

• 8

  BittleK.El-GayarO. (2025). Generative ai and academic integrity in higher education: a systematic review and research agenda. Information16, 296. 10.3390/info16040296

  • CrossRef
  • Google Scholar

• 9

  ChanC. K. Y.CollotonT. (2024). Redesigning Assessment in the Ai Era. 1 Ed.Abingdon and New York, NY: Routledge.

  • Google Scholar

• 10

  ChiuT. K. F. (2024). The impact of generative ai (genai) on practices, policies and research direction in education: a case of Chatgpt and midjourney. Interact. Learn. Environ.32, 6187–6203. 10.1080/10494820.2023.2253861

  • CrossRef
  • Google Scholar

• 11

  CoelhoR.LoganC.AgkünS.PeaR. (2024). “A design space for articulating and addressing the risks of integrating generative ai in education,” in Proceedings of the 19th International Conference of the Learning Sciences-ICLS 2025. (International Society of the Learning Sciences), 1679–1683. 10.22318/icls2025.837880

  • CrossRef
  • Google Scholar

• 12

  CorbinT.DawsonP.LiuD. (2025). Talk is cheap: why structural assessment changes are needed for a time of GenAI. Assess. Eval. High. Educ.50 (7), 1087–1097. 10.1080/02602938.2025.2503964

  • CrossRef
  • Google Scholar

• 13

  DawsonP.BearmanM.DollingerM.BoudD. (2024). Validity matters more than cheating. Assess. Eval. High. Educ.49, 1005–1016. 10.1080/02602938.2024.2386662

  • CrossRef
  • Google Scholar

• 14

  FrancisN. J.JonesS.SmithD. P. (2024). Generative ai in higher education: balancing innovation and integrity. Br. J. Biomed. Sci.81, 14048. 10.3389/bjbs.2024.14048

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 15

  HanJ.YangY.LiuG. (2025). Are teachers assessing work written by students or by ai? A rapid literature review of research on detecting content generated by generative ai. Eur. J. Educ.60, E70240. 10.1111/ejed.70240

  • CrossRef
  • Google Scholar

• 16

  HolmesW.MiaoF. (2023). Guidance for Generative Ai in Education and Research. Paris: UNESCO: Publishing.

  • Google Scholar

• 17

  HuynhL.LeT. H.DangB.AnB. T.VuC. T.NguyenA. (2025). Mindset matters: fostering Teachers’ responsible ai use through professional development. J. New Approaches Educ. Res.14, 23. 10.1007/s44322-025-00043-y

  • CrossRef
  • Google Scholar

• 18

  KolbD. A. (2014). Experiential Learning: Experience as the Source of Learning and Development. Upper Saddle River, NJ: Ft Press.

  • Google Scholar

• 19

  LoL. S. (2023). The art and science of prompt engineering: a new literacy in the information age. Int. Ref. Serv. Q.27, 203–210. 10.1080/10875301.2023.2227621

  • CrossRef
  • Google Scholar

• 20

  LuoJ. (2024). A critical review of genai policies in higher education assessment: a call to reconsider the “originality” of Students’ work. Assess. Eval. High. Educ.49, 651–664. 10.1080/02602938.2024.2309963

  • CrossRef
  • Google Scholar

• 21

  MengeshaZ.EstelloreJ.SmithJ.GeorgeM. S.McfarlaneR.PalmarJ. (2025). Integrating generative AI into public health education: a mixed methods evaluation of student experiences. [Preprint]. 10.21203/rs.3.rs-7821235/v1

  • CrossRef
  • Google Scholar

• 22

  NikolicS.WentworthI.SheridanL.MossS.DuursmaE.JonesR. A.et al (2024). A systematic literature review of attitudes, intentions and behaviours of teaching academics pertaining to ai and generative ai (genai) in higher education: an analysis of genai adoption using the utaut framework. Australas. J. Educ. Technol.40, 56–75. 10.14742/ajet.9643

  • CrossRef
  • Google Scholar

• 23

  PalviaS.AeronP.GuptaP.MahapatraD.ParidaR.RosnerR.et al (2018). Online education: worldwide status, challenges, trends, and implications. J. Glob. Inf. Technol. Manag.21, 233–241. 10.1080/1097198X.2018.1542262

  • CrossRef
  • Google Scholar

• 24

  QuY.TanM. X. Y.WangJ. (2024). Disciplinary differences in undergraduate students’ engagement with generative artificial intelligence. Smart Learn. Environ.11, 51. 10.1186/s40561-024-00341-6

  • CrossRef
  • Google Scholar

• 25

  QuinceZ.NikolicS. (2026). Student identification of the social, economic and environmental implications of using generative artificial intelligence (GenAI): identifying student ethical awareness of ChatGPT from a scaffolded multi-stage assessment. Eur. J. Eng. Educ.51 (1), 43–62. 10.1080/03043797.2025.2482830

  • CrossRef
  • Google Scholar

• 26

  SalmonG.2012. E-Moderating: The Key to Online Teaching and Learning, New York and London: Routledge.

  • Google Scholar

• 27

  SmithS. M.TateM.FreemanK.WalshA.Ballsun-StantonB.LaneM. (2026). A university framework for the responsible use of generative AI in research. J. High. Educ. Policy Manag.48 (1), 17–36. 10.1080/1360080X.2025.2509187

  • CrossRef
  • Google Scholar

• 28

  SuJ.YangW. (2023). Unlocking the power of Chatgpt: a framework for applying generative ai in education. ECNU Rev. Educ.6, 355–366. 10.1177/20965311231168423

  • CrossRef
  • Google Scholar

• 29

  TranM.BalasooriyaC.JonnagaddalaJ.LeungG. K.-K.MahboobaniN.RamaniS.et al (2025). Situating governance and regulatory concerns for generative artificial intelligence and large language models in medical education. NPJ Digit. Med.8, 315. 10.1038/s41746-025-01721-z

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 30

  VygotskyL. S. (1978). Mind in Society: The Development of Higher Psychological Processes. Cambridge, MA: Harvard University Press.

  • Google Scholar

• 31

  WangJ.LiJ. (2024). Artificial intelligence empowering public health education: prospects and challenges. Front. Public. Health.12, 1389026. 10.3389/fpubh.2024.1389026

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 32

  WHO (2024). Global Competency and Outcomes Framework for the Essential Public Health Functions. Geneva: World Health Organization.

  • Google Scholar

• 33

  WilliamsA. (2025). Integrating artificial intelligence into higher education assessment. Intersect: J. Intersect. Assess. Learn.6 (1), 128–154. 10.61669/001c.131915

  • CrossRef
  • Google Scholar

• 34

  XiaQ.WengX.OuyangF.LinT. J.ChiuT. K. F. (2024). A scoping review on how generative artificial intelligence transforms assessment in higher education. Int. J. Educ. Technol. High. Educ.21, 40. 10.1186/s41239-024-00468-z

  • CrossRef
  • Google Scholar

• 35

  ZainuddinN. M. M.AzmiN. F. M.YusoffR. C. M.ShariffS. A.HassanW. A. W. (2020). Enhancing classroom engagement through padlet as a learning tool: a case study. Int. J. Innov. Comput.10 (1). 10.11113/ijic.v10n1.250

  • CrossRef
  • Google Scholar

• 36

  ZhanY.BoudD.DuZ. (2025). Designing for authentic assessment: a scoping review. High Educ. 10.1007/s10734-025-01588-9

  • CrossRef
  • Google Scholar

• 37

  ZhangX.ZhangP.ShenY.LiuM.WangQ.GaševićD.et al (2024). A systematic literature review of empirical research on applying generative artificial intelligence in education. Frontiers of Digital Education1, 223–245. 10.1007/s44366-024-0028-5

  • CrossRef
  • Google Scholar