2026 AAUT Program Award Evidence

Primary evidence — presented first

Core SAGE studies and what each one evidences

Each study is mapped to the claims, criteria and scholarly foundations it supports. S1–S12 are internal SM1 source identifiers used only on this evidence website; the nomination itself points assessors to the relevant SM1-A1 to SM1-D7 claim anchor.

SM1 source	Study	Direct evidence contribution	AAUT criteria and theory anchors	Record
P0Framework paper	SAGE framework paperM. Elkhodr and E. Gide, “Structured AI-guided education (SAGE): A six-step pedagogy for AI orchestration and assessment assurance in higher education,” STEM Education, vol. 6, no. 5, pp. 974–997, 2026. doi: 10.3934/steme.2026039.	Canonical peer-reviewed account of the SAGE six-step pedagogy and its distributed Assurance by Design architecture.	Foundational framework	DOI ↗
S1	ICT students’ perceptions towards ChatGPT: An experimental reflective lab analysisSTEM Education, 2023	Origin study: student engagement and performance across three ICT case studies; identified over-reliance and the need for structured evaluation and reflection.	ACDConstructivismBloomSelf-regulation	DOI ↗
S2	The role and impact of ChatGPT in educational practices: Insights from an Australian higher education case studyDiscover Education, 2024	Cross-institutional transfer and student-rated benefit; demonstrates that the approach could move beyond its originating classroom and educators.	ABCDSituated learningDesign-based research	DOI ↗
S3	The SAGE framework for developing critical thinking and responsible generative AI use in cybersecurity educationDiscover Education, 2025	Cybersecurity cohorts evaluated AI advice against regulatory, organisational and professional constraints; evidence of critical thinking, completion and discipline-specific judgement.	ACDSituated learningConstructive alignmentEvaluative judgement	DOI ↗
S4	AI leads, humans lead, or collaborate? Empirical findings and the SAGE roadmap for systems analysis and design educationSTEM Education, 2026	Eighteen groups analysed from a cohort of 281 across five delivery contexts: selective AI orchestration, systematic error detection, context-sensitive rejection and accessibility improvement.	ACDSituated learningEvaluative judgementConstructive alignment	DOI ↗
S5	From Permission to Pedagogy: The Structured AI-Guided Education Assessment Policy (SAGE-AP)Education Sciences, 2026	Translates broad AI permission levels into task-level guidance on verification, declaration, student judgement and defensible authorship.	ACDAI literacyConstructive alignmentAssessment design	DOI ↗
S6	Embedding assurance within learning: Empirical evidence from SAGE for repositioning take-home assessmentSTEM Education, 2026	Audit evidence showed that only three of twenty-five process logs were substantially auditable; supports the shift from documentation policing to assurance embedded in learning.	ACDAssessment securityProgrammatic assessmentAssurance by design	DOI ↗
S7	Eliciting Student Authority over AI Feedback: The AI as Critic Mechanism in SAGE-Based Systems Analysis and Design EducationTechnology, Knowledge and Learning, 2026, accepted	Examines how students accept, modify or reject AI critique and retain authority over the final disciplinary decision.	ACDSelf-regulationEvaluative judgementMetacognition	DOI ↗
S8	How first-year students actually use ChatGPT in permitted assessmentsPreprint / submitted, 2026	First-year cohort (n=167): 73% verified systematically and 81% revised deeply; engaging more iteratively was the most frequently selected intended change. The study identifies verification gaps and student demand for practical guidance.	ACDSelf-regulated learningMetacognitionAI literacy	Preprint ↗
S9	Assurance by design: embedding the SAGE Defend step in AI-integrated higher education assessmentFrontiers in Education, vol. 11, art. 1872630, 2026	Formalises discipline-matched Defend activities and fair, visible verification of student learning within AI-integrated assessment.	ACDAssessment securityProgrammatic assessmentConstructive alignment	DOI ↗
S10	Supervising Research in the Age of Generative AI: The SAGE Research FrameworkUnder review, 2026	Extends structured verification, disclosure, reflection and defensible authorship into research candidature and supervision.	ACDSelf-regulationResearch integrityEvaluative judgement	Under review
S11	Promoting Critical AI Literacy in Transnational Computing Education Through a SAGE-Guided InterventionICAIES 2026, accepted and presented	UK–China Level 4 computing implementation: confidence rose from 2.84 to 3.63/5 and 57.3% correctly identified and justified an AI error.	ABCDSituated learningCritical AI literacy	Presented
S12	Developing Critical AI Literacy through SAGE in Transnational Computing EducationUnder review, 2026	Fuller account of the international intervention, disciplinary error-checking and transferability within a UK–China higher education programme.	ABCDSituated learningDesign-based research	Under review

Broader scholarly programme

GenAI in education — broader research base

The 12 studies above are the direct SAGE evidence. Together with the related outputs below, they form a 40+ publication research base spanning GenAI in education, AI literacy, assessment reform, curriculum design and interdisciplinary applications. This broader corpus demonstrates sustained scholarship; it does not replace the criterion-specific evidence.

12core SAGE studies mapped directly to the award criteria

40+publications and related scholarly outputs across the broader programme

100,000+collective views reported across the broader research corpus in the application

View 28 additional publications in the broader research corpus

M. Elkhodr and E. Gide, Eds., Generative Artificial Intelligence Empowered Learning: A New Frontier in Educational Technology, 1st ed. New York, NY: Taylor and Francis, 2025, 248 pp. doi: 10.1201/9781003422433. eBook ISBN: 9781003422433
R. Jamal Eddine, E. Gide, and A. Al-Sabbagh, "Generative AI in higher education: A cross-sector analysis of ChatGPT's impact on STEM, social sciences, and healthcare," STEM Education, vol. 5, no. 5, pp. 757–801, 2025. doi: 10.3934/steme.2025035
K. Wangsa, S. Karim, E. Gide, and M. Elkhodr, "A systematic review and comprehensive analysis of pioneering AI chatbot models from education to healthcare: ChatGPT, Bard, Llama, Ernie and Grok," Future Internet, vol. 16, no. 7, art. 219, 2024. doi: 10.3390/fi16070219
K. Wangsa, R. Sandu, S. Karim, M. Elkhodr, and E. Gide, "A systematic review and analysis on the potentials and challenges of GenAI chatbots in higher education," in Proc. 21st Int. Conf. Information Technology Based Higher Education and Training (ITHET), Paris, France, Nov. 2024, pp. 1–7. doi: 10.1109/ITHET61869.2024.10837608
A. Al Tawara, J. El-Den, E. Gide, and Y. Sebastian, "A systematic review and comprehensive analysis of AI-enabled re-skilling and upskilling in education: Transformative strategies for the future," in Proc. 21st Int. Conf. Information Technology Based Higher Education and Training (ITHET), Paris, France, Nov. 2024, pp. 1–10. doi: 10.1109/ITHET61869.2024.10837638
A. Al Tawara, E. Gide, and J. El-Den, "Systematic review and comprehensive analysis of integrating human-centered AI in higher education: Enhancing teaching, learning, and ethics," in Proc. 12th Int. Conf. Future Internet of Things and Cloud (FiCloud), Aug. 2025, pp. 305–312. doi: 10.1109/FiCloud61071.2025.00051
H. Ranasinghe, E. Gide, and M. Elkhodr, "The significance of GenAI empowered ERP systems course teaching in quality education," in Proc. 21st Int. Conf. Information Technology Based Higher Education and Training (ITHET), Paris, France, Nov. 2024, pp. 1–7. doi: 10.1109/ITHET61869.2024.10837679
G. Chaudhry, E. Gide, E. Yadegaridehkordi, and R. Tumpa, "Generative AI-powered teaching and learning in engineering and project management higher education: A systematic review," in Joint International Conference on AI, Big Data and Blockchain, Cham, Switzerland: Springer Nature, Aug. 2025, pp. 99–113.
E. Gusman, E. Gide, M. Elkhodr, and G. Chaudhry, "The benefits and challenges of using artificial intelligence in teaching English as a foreign language in higher education," in Proc. 21st Int. Conf. Information Technology Based Higher Education and Training (ITHET), Paris, France, Nov. 2024, pp. 1–7. doi: 10.1109/ITHET61869.2024.10837597
E. Gusman, E. Gide, G. Chaudhry, and M. Elkhodr, "A comprehensive review to identify the challenges and opportunities of using digital technology in English teaching in higher education," in International Society for Technology, Education, and Science, 2023.
R. Sandu, E. Gide, S. Karim, and P. Singh, "A framework for GenAI-empowered curriculum and learning resources: A case study from an Australian higher education," in Proc. 21st Int. Conf. Information Technology Based Higher Education and Training (ITHET), Paris, France, Nov. 2024, pp. 1–8. doi: 10.1109/ITHET61869.2024.10837623
N. Sandu and E. Gide, "Adoption of AI-Chatbots to enhance student learning experience in higher education in India," in Proc. 18th Int. Conf. Information Technology Based Higher Education and Training (ITHET), Sept. 2019, pp. 1–5. doi: 10.1109/ITHET46829.2019.8937382
M. Elkhodr, K. Wangsa, E. Gide, and S. Karim, "A systematic review and multifaceted analysis of the integration of artificial intelligence and blockchain: Shaping the future of Australian higher education," Future Internet, vol. 16, no. 10, art. 378, 2024. doi: 10.3390/fi16100378
M. Elkhodr, E. Gide, and N. Pandey, "Enhancing mental health support for international students: A digital framework for holistic well-being in higher education," STEM Education, vol. 4, no. 4, pp. 466–488, 2024. doi: 10.3934/steme.2024025
N. Abbasi, E. Gide, P. Kalutara, and P. Lawrence, "Barriers and opportunities in online delivery of architecture and building design studios: Australian educators' perspectives," 2024. [Online]. Available: Architecture Science
R. Jamal Eddine, E. Gide, and A. Al-Sabbagh, "Systematised evidence mapping of generative artificial intelligence (GenAI) and digital divide phenomena in higher education," Discover Computing, vol. 29, art. 157, 2026. doi: 10.1007/s10791-026-10044-w
A. Azra, E. Gide, R. M. X. Wu, S. Karim, and R. Sandu, "Artificial Intelligence Enhancing Learning and Satisfaction in Higher Education," in CQU Scholarship of Tertiary Teaching Online Conference, Australia, 2020.
A. M. Al Tawara, E. Gide, and M. El Khodr, "A Systematic Review and Framework for AI-Driven Transformation from E-Business to AI-Business in Australian SMEs," in Proc. 22nd Int. Conf. Information Technology Based Higher Education and Training (ITHET), May 2026.
A. M. Al Tawara, E. Gide, and M. El Khodr, "Development of an AI-Business Transformation Framework for Australian SMEs: A PRISMA-Based Systematic Review and Design Science Approach," in Proc. 22nd Int. Conf. Information Technology Based Higher Education and Training (ITHET), May 2026.
R. Agaloos, E. Gide, and M. ElKhodr, "An Analysis of GenAI Adoption Effectiveness in Philippines Healthcare SMEs," in Proc. 22nd Int. Conf. Information Technology Based Higher Education and Training (ITHET), Lilihammer, Norway, 2026, pp. 1–7. doi: 10.1109/ITHET69978.2026.11585098
A. Al Tawara, E. Gide, and M. El Khodr, "Development of an AI-Business Transformation Framework for Australian SMEs: A PRISMA-Based Systematic Review and Design Science Approach," in Proc. 22nd Int. Conf. Information Technology Based Higher Education and Training (ITHET), Lilihammer, Norway, 2026, pp. 1–9. doi: 10.1109/ITHET69978.2026.11584989
A. Al Tawara, E. Gide, and M. El Khodr, "A Systematic Review and Framework for AI-Driven Transformation from E-Business to AI-Business in Australian SMEs," in Proc. 22nd Int. Conf. Information Technology Based Higher Education and Training (ITHET), Lilihammer, Norway, 2026, pp. 1–8. doi: 10.1109/ITHET69978.2026.11585085
R. Agaloos, E. Gide, and M. ElKhodr, "Investigating GenAI Adoption Readiness of Healthcare SMEs in Phillippines," in Proc. 22nd Int. Conf. Information Technology Based Higher Education and Training (ITHET), Lilihammer, Norway, 2026, pp. 1–7. doi: 10.1109/ITHET69978.2026.11584974
H. Ranasinghe, E. Gide, and M. ElKhodr, "A Systematic Review of AI-ERP Integration for Educational Data Analytics and Institutional Insights," in Proc. 22nd Int. Conf. Information Technology Based Higher Education and Training (ITHET), Lilihammer, Norway, 2026, pp. 1–8. doi: 10.1109/ITHET69978.2026.11585168
A. Azra, E. Gide, S. Karim, and M. ElKhodr, "A Comprehensive Analysis to Identify the Opportunities and Challenges of GenAI-Driven Research in Higher Education," in Proc. 22nd Int. Conf. Information Technology Based Higher Education and Training (ITHET), Lilihammer, Norway, 2026, pp. 1–10. doi: 10.1109/ITHET69978.2026.11585029
H. Ranasinghe, E. Gide, and M. ElKhodr, "A Systematic Review of AI Applications in ERP Systems in Higher Education," in Proc. 22nd Int. Conf. Information Technology Based Higher Education and Training (ITHET), Lilihammer, Norway, 2026, pp. 1–9. doi: 10.1109/ITHET69978.2026.11585080
S. Karim, E. Gide, A. Azra, M. Chowdhury, and R. Sandu, "The Impact of AI on the Quality of Learning and Teaching in Higher Education," in Proc. 22nd Int. Conf. Information Technology Based Higher Education and Training (ITHET), Lilihammer, Norway, 2026, pp. 1–5. doi: 10.1109/ITHET69978.2026.11585171
A. Ul Hassan, G. Chaudhry, and E. Gide, "Using Generative AI to Educate and Train SMEs in Adopting Renewable Energy in Regional Australia," in Proc. 22nd Int. Conf. Information Technology Based Higher Education and Training (ITHET), Lilihammer, Norway, 2026, pp. 1–10. doi: 10.1109/ITHET69978.2026.11585089

Publications 1–12 are the core studies mapped above. The same full record is maintained on the SAGE publications page ↗.

Criterion A — publications plus direct student evidence

A. Positively impacted on student learning, student engagement or the overall student experience for a period of no less than three years

The empirical studies are the primary evidence. Dashboards, de-identified student decisions, result histories and independent educator observations show the underlying student behaviour and implementation context.

SM1-A1Sustained impact across levels, disciplines and delivery modes

Successive SAGE studies document progression from initial ICT classroom use to cybersecurity, systems analysis, first-year communication, research candidature and international technical tasks. Core SAGE was implemented across 13 CQUniversity units over ten teaching terms from Term 3 2022 to Term 2 2026, representing 2,210 unit enrolments and more than 1,500 individual students, together with cross-institutional and international implementations.

SM1 SAGE sources: S1, S2, S3, S4, S7, S8, S10, S11 and S12.

Implementation records: unit result histories for COIT20268, COIT12212, COIT20263 and COIT20248.

Evaluation cycle: observed student difficulty led to a documented program refinement in each phase.

SM1-A2First-year students learned to verify and revise rather than accept AI output

The n=167 first-year study records 73% systematic verification and 81% deeper revision; engaging more iteratively was the most frequently selected intended change (46.7%). Verification remained challenging, with 77.8% identifying accuracy checking as the hardest part, showing both active engagement and the continuing need for guided practice.

SM1 SAGE source: S8.

Interactive evidence: the public first-year dashboard presents the response distributions and learner profiles.

Independent corroboration: Dr Ahmedi Azra reported that students valued the clarity and structure and were taught appropriate AI use rather than approaching it mainly through fear of cheating.

Public translation (corroborating): PC1 and PC2 translate the student demand for practical guidance to higher-education audiences.

SM1-A3Advanced students demonstrated selective disciplinary judgement

Cybersecurity and systems-analysis students evaluated AI recommendations against regulatory, organisational, usability and task constraints. Strong acceptance of valid advice alongside rejection of contextually inappropriate advice indicates selective orchestration rather than indiscriminate compliance.

SM1 SAGE sources: S3, S4 and S7.

De-identified artefacts: the systems-analysis dataset records tests, AI recommendations, acceptance/rejection decisions and student reasons.

Corroboration: tutor feedback and an Atlassian engineering review of the authentic cybersecurity case.

Public translation (corroborating): PC9 reports the cybersecurity implementation for a wider science audience.

SM1-A4The learning model transferred beyond the originating classroom

The 2024 cross-institutional study included undergraduate and postgraduate students from business and ICT settings. The UK-China implementation then applied SAGE to a graph-theory problem in which students had to identify and justify an AI error.

SM1 SAGE sources: S2, S11 and S12.

Australian implementation record: Dr Raj Sandu documented the multi-institution participant context and classroom/tutorial activities.

International outcome: confidence increased from 2.84 to 3.63/5 and 57.3% correctly identified and justified the technical error.

SM1-A5SAGE principles extended into research candidature

SAGE-R applies structured verification, reflection and defensible authorship to research supervision. The first graduate associated with this agenda completed a thesis rated by both examiners as outstanding quality and within the top 10% of the field.

SM1 SAGE source: S10.

Corroborating record: the Dean, School of Graduate Research congratulatory letter.

Careful interpretation: the record corroborates the graduate outcome; it does not attribute that outcome solely to SAGE.

First-year verification dashboard

Interactive evidence for the n=167 study, including verification, revision, challenge and learner-profile findings.

Open dashboard ↗

Systems-analysis decision dataset

De-identified group artefacts showing AI recommendations accepted, rejected or deferred with disciplinary reasons.

Open dataset ↗

Screenshot of first-year student survey results supporting the SAGE verification study

First-year student survey evidence

Direct response evidence associated with the public dashboard and S8.

Open interactive dashboard

Educator email reporting student appreciation of the clarity and structure of the SAGE redesign

Independent first-year educator observation

Students appreciated the redesign's clarity and structure and received practical guidance on appropriate AI use.

Cross-institutional implementation record

Documents undergraduate/postgraduate, ICT/business and multi-institution participation behind S2.

Tutor feedback reporting that the redesigned tutorial was working well

Independent tutor feedback

Corroborates improved alignment between tutorial activity and lectures in the systems-analysis redesign.

Criterion B — official adoption and independent recognition

B. Gained recognition from colleagues, the institution and/or the broader community

The evidence distinguishes formal institutional incorporation, national resource listing, independent educator uptake, cross-institutional implementation, international engagement and scholarly/public reach.

SM1-B1CQUniversity formally incorporated SAGE into its assessment model

The evidence should be read as a sequence. The 2026 Guided-Assured Assessment Model Educator Guide first establishes SAGE inside the official model by naming the framework, reproducing its six-step cycle and mapping SAGE orchestration levels to university assessment guidance. A 30 July 2026 implementation notice then shows the Guided-Assured model moving into operational use across Term 3 unit-profile updates, while the Academic Learning Centre directly adopted the named SAGE GenAI-101 module in its student-facing GenAI resources. Together, these records demonstrate formal incorporation, operational rollout and central learning-support adoption.

1. Official guide: view the SAGE six-step evidence and orchestration-level mapping ↗.

2. Operational rollout, 30 July 2026: view the redacted Term 3 implementation notice ↗.

3. Direct central-service adoption: view the Academic Learning Centre’s student-facing SAGE GenAI-101 listing ↗.

Staff development: view Dr Daniel Maddock’s GenAI for Educators email ↗.

Careful interpretation: the rollout notice operationalises the Guided-Assured model across Term 3 units; it is not presented as proof that every unit must use SAGE specifically.

SM1-B2Educators outside ICT independently selected SAGE resources for their students

A Business Ethics lecturer independently discovered the Generative AI Literacy and Numeracy Module, described it as clear, practical, accessible and tangible for students, and committed to sharing it with her class.

Evidence: view the redacted email from Dr Renata Klafke ↗, School of Business and Law.

Value: cross-disciplinary recognition and intended uptake without mandate or direct promotion.

Careful wording: evidence of committed uptake; completed student outcomes are not claimed.

SM1-B3Three SAGE resources received national TEQSA listing

Archived TEQSA pages show the implementation guide under Academic integrity and assessment reform, and the literacy/numeracy module and first-year student-use resource under Student resources and support.

Evidence: three separate national resource entries.

Supporting resources: S5, S8 and the evidence-based implementation guide.

Careful wording: national recognition and resource listing, not national adoption.

Official TEQSA Higher Education Integrity Unit email dated 27 February 2026 endorsing the SAGE Framework and resources for inclusion in the Gen AI Knowledge Hub.

TEQSA review and endorsement

On 27 February 2026, after reviewing the SAGE Framework, accompanying resources and dashboard, TEQSA’s Higher Education Integrity Unit advised that the resources would be “an excellent addition to the TEQSA Gen AI Knowledge Hub”. The resources were subsequently published through three separate national Hub listings.

Source: TEQSA Higher Education Integrity Unit official email, 27 February 2026.

SM1-B4External academics initiated and implemented cross-institutional work

Dr Raj Sandu's record supports the Australian cross-institutional study. Associate Professor Maged Fakirah independently approached the team after identifying SAGE as a structured, evidence-based framework suitable for UK-Chinese transnational education, leading to the documented technical implementation and accepted conference work.

Australian evidence: S2 and implementation email.

International evidence: S11 and S12, initial unsolicited approach and support/grant letter.

Independent recognition: University of Warwick Education Portal case study.

SM1-B5The research and resources show substantial scholarly and practitioner reach

Dated publisher, Google Scholar and Zenodo records provide transparent evidence of readership, citation and resource uptake. Platform-specific metrics are labelled rather than combined as if they were directly equivalent.

Selected publisher reach: view the dated records for the 2023 ICT study, 2024 cross-institutional study and systematic review ↗.

Selected Google Scholar records: open the Google Scholar publication profile ↗.

Open resource uptake: view the implementation guide metrics ↗.

Public and sector commentary: PC1, PC2, PC3, PC4, PC5, PC6, PC8 and PC9 translate the research for sector and community audiences.

CQUniversity Guided-Assured Assessment Model page 17 naming SAGE and presenting its six steps

Official CQUniversity adoption: six-step cycle

Page 17 names SAGE and incorporates Generate, Evaluate, Refine, AI Critic, Reflect and Defend.

Open official webpage Open guide PDF

CQUniversity guide page 12 mapping SAGE student orchestration levels

Official CQUniversity adoption: orchestration levels

Page 12 maps SAGE Student Orchestration Levels to guidance, permitted use and student submission requirements.

Operational rollout of the Guided-Assured model

On 30 July 2026, Term 3 Unit Coordinators were advised that the Guided-Assured Assessment Model would be implemented from Term 3 and that assessment structures must be updated through the University Unit Profile process. Read after the official guide above, this shows the institutional mechanism through which SAGE-informed guided-assurance practice can move into operational use.

CQUniversity Academic Learning Centre student-facing GenAI resources page incorporating the SAGE GenAI-101 Generative AI Literacy and Numeracy module

Direct Academic Learning Centre adoption

CQUniversity’s Academic Learning Centre incorporated the SAGE GenAI-101: Generative AI Literacy and Numeracy module into its student-facing GenAI Resources and Workshops site, extending SAGE beyond individual units into central academic-learning support.

Business Ethics lecturer email praising and committing to share the SAGE literacy and numeracy module

Independent cross-disciplinary uptake

Unsolicited recognition from a Business Ethics lecturer outside ICT.

Unsolicited international email proposing UK-Chinese transnational implementation of SAGE

Unsolicited international approach

Initial external contact proposing SAGE deployment and transferability testing in UK-Chinese TNE contexts.

Open subsequent support letter

16,641publisher-recorded views for the 2023 ICT study, plus 922 PDF downloads

16kpublisher-recorded accesses for the 2024 cross-institutional study

18,676publisher-recorded views for the systematic review

507downloads of version 3 of the SAGE implementation guide, from 616 views

Google Scholar screenshot showing selected citation counts

Selected Google Scholar citation counts

176 citations for the origin study, 112 for the systematic review and 95 for the cross-institutional case study at capture.

Open Google Scholar profile ↗

Zenodo screenshot showing version 3 implementation guide views and downloads

Open-resource uptake

Version 3 of the implementation guide recorded 616 views and 507 downloads, alongside use of related SAGE tools.

Publisher metrics and Google Scholar counts use different databases and are therefore displayed separately. The values are dated evidence snapshots and should be refreshed shortly before submission.

Corroborating public translation

Public & sector commentary

These numbered sources show how SAGE research and its implications have been translated for higher-education, professional and community audiences. They evidence dissemination, reach and sector engagement; the primary evidence remains the peer-reviewed studies and implementation records above.

PC1

THE Campus · 23 April 2026Students are asking for AI guidance, not just policy ↗

Why higher education must move from blanket policy statements to structured, practical guidance that helps students use AI responsibly and with critical judgement.

PC2

HERDSA Connect · 17 June 2026Students are asking for AI guidance, not just policy: A comprehensive, practical framework for AI integration ↗

An adaptation for the HERDSA community focused on embedding structured AI guidance in assessment.

PC3

Future Campus · 11 April 2026AI era must not become an excuse to default to low-tech exams ↗

Commentary arguing that low-tech, high-surveillance assessment is an impoverished response to AI.

PC4

CQUniversity News · 4 March 2026Teaching students to think with AI — not cheat with it ↗

A CQUniversity feature on making student AI use, judgement and verification visible in assessment.

PC5

CQUniversity News · 24 March 2026CQUniversity leads shift in AI-ready education ↗

Coverage of the SAGE-based approach and its focus on developing AI-ready graduates.

PC6

The Educator · 13 March 2026Opinion: Teaching students to think with AI — not cheat with it ↗

Reframes the AI debate from integrity threat to a scaffold for critical thinking and defensible judgement.

PC7

CQUniversity News · 13 May 2026Smart cities of the future could think for themselves ↗

Public translation of AI and human-oversight themes that are central to the SAGE programme.

PC8

Gladstone Today · 28 March 2026CQUniversity leads shift in AI-ready education ↗

Regional coverage of SAGE-led AI-integrated assessment and graduate readiness.

PC9

SciEnMag · 27 November 2025Empowering cybersecurity education with responsible AI framework ↗

Science-media coverage of the cybersecurity implementation of SAGE.

PC10

LinkedIn · 14 March 2026Protect your research in the age of generative AI: Rethinking the preprint ↗

Professional commentary on research integrity, authorship and preprint practices in the age of generative AI.

References PC1–PC10 are used throughout Criteria A–D as corroborating translation and reach evidence.

Criterion C — innovation evidenced through use and adoption

C. Shown creativity, imagination and/or innovation

The innovation is not the existence of six labels. It is the integration of guided AI use, disciplinary judgement and direct assurance within one adaptable learning-and-assessment architecture.

SM1-C1A six-step architecture joins learning with assurance

Generate, Evaluate, Refine, AI Critic and Reflect develop capability in open conditions; Defend provides a discipline-appropriate check of individual understanding. The architecture has moved from empirical classroom design into CQUniversity's official assessment guidance.

SM1 SAGE sources: S1, S3, S4 and S9.

Official incorporation: CQUniversity Guided-Assured Assessment Model, page 17.

Novel contribution: guidance and assurance are designed as a connected sequence rather than separate policy and policing activities.

Public translation (corroborating): PC4 and PC6 explain the architecture's focus on critical thinking and visible student judgement.

SM1-C2AI Critic makes student authority visible

Students ask AI to critique their work and then judge the critique. They must accept, reject or modify advice with reasons, making authority calibration and evaluative judgement visible.

SM1 SAGE sources: S4 and S7.

Direct artefacts: the systems-analysis dataset records decisions and reasons.

Theory: evaluative judgement, metacognition and self-regulated learning.

SM1-C3Assurance by design replaces unreliable retrospective proof

After the process-log audit found only three of twenty-five submissions substantially auditable, the program shifted toward task-matched Defend activities and articulated assurance debt and assurance by design.

SM1 SAGE sources: S6 and S9.

Supporting tool: SAGE Defend Planner.

Student benefit: ownership is demonstrated through the learning activity rather than inferred from a final artefact or potentially unreliable log.

Sector debate: PC3 positions assurance by design against defaulting to low-tech examinations.

SM1-C4SAGE developed into an integrated, portable program

SAGE Pedagogy develops student judgement; SAGE-AP converts permission into assessment requirements; SAGE-R extends responsible authorship into research; and SAGE-I is the planned workplace translation. The core is adapted to disciplinary evidence and defence formats rather than copied rigidly.

SAGE Pedagogy: S1, S2, S3, S4, S7 and S8.

SAGE-AP and Defend: S5, S6 and S9.

SAGE-R: S10 and graduate evidence. SAGE-I: retained as emerging/pending evidence.

SM1-C5Authenticity was tested against professional practice

The Head of Engineering at Atlassian reviewed the cybersecurity assessment case and confirmed alignment with contemporary industry risks, challenges and opportunities, supporting the authenticity of the context in which students judged AI advice.

SM1 SAGE source: S3.

Independent corroboration: redacted industry review.

Value: validates the professional context, not merely the novelty of the pedagogy.

See AI judgement in student artefacts

The public dataset shows recommendations, decisions and reasons rather than only reporting summary percentages.

Open systems-analysis evidence ↗

Design assurance activities

The SAGE Defend Planner translates the innovation into practical assessment design choices.

Open Defend Planner ↗

Criterion D — theory mapped to decisions and studies

D. Drawn on the scholarly literature on teaching and learning to inform the development of initiatives, programs and/or practice

The nomination cites the independent scholarly foundations directly. This SM1 mapping shows how those foundations informed specific SAGE design decisions and where the resulting practices were evaluated or formalised across the programme. S1–S12 are supporting SAGE sources within SM1 rather than nomination reference numbers.

Constructivism and revised Bloom’s taxonomySM1-D1

Students begin with a draft or problem representation, then analyse, evaluate, revise and defend rather than submit AI output as an answer.

Mapped studies: S1, S2, S3, S4 and S7. Programme decision: progression from Generate to Evaluate, Refine and Defend.

Situated learningSM1-D2

AI output is judged against the standards, constraints and practices of the student’s discipline rather than generic accuracy alone.

Mapped studies: S3, S4, S11 and S12. Programme decision: cybersecurity frameworks, organisational constraints and mathematical optimisation principles become evidence anchors.

Self-regulated learning and metacognitionSM1-D3

Students monitor their reliance on AI, explain changes, reflect on weaknesses and plan a more effective subsequent attempt.

Mapped studies: S1, S7, S8 and S10. Programme decision: explicit reflection, iteration and disclosure of accepted, modified and rejected output.

Constructive alignmentSM1-D4

AI conditions, learning activities, disciplinary evidence and the method of assurance are aligned with the intended learning outcomes.

Mapped studies: S3, S4, S5, S6 and S9. Programme decision: task-level guidance and discipline-matched Defend activities.

Assessment security and programmatic assessmentSM1-D5

Assurance is accumulated through appropriate checks across learning rather than concentrated in one final examination or inferred from process logs.

Mapped studies: S6 and S9. Programme decision: assurance by design and management of assurance debt.

Evaluative judgement and AI literacySM1-D6

Students learn to judge the quality, relevance and consequences of AI output and to retain authority over the final decision.

Mapped studies: S3, S4, S5, S6, S7, S8, S9, S11 and S12. Programme decision: AI Critic, evidence anchoring and reasoned acceptance/rejection.

Design-based researchSM1-D7

Each implementation identified a student problem, introduced a change, collected classroom evidence and informed the next iteration.

Mapped studies: S1, S2, S3, S4, S5, S6, S7, S8, S9, S10, S11 and S12. Programme decision: a cumulative programme evolving from classroom pedagogy to SAGE-AP, Defend, SAGE-R and international adaptation.

How to read the mapping: these are best-fit theory anchors across a cumulative research programme. The empirical papers commonly draw on several foundations; the mapping is intended to show how scholarship informed specific design decisions, not to force each paper into a single theory.

Public translation: PC1, PC2, PC3 and PC10 carry the scholarly reasoning into assessment-policy, research-integrity and sector-practice discussions.

Evidence archive

Documents and dated screenshots used on this page

The main page shows only the strongest evidence. The archive below provides the complete supporting trail without crowding the criterion sections.

Institutional adoption and national recognition

Official CQUniversity guide, staff-development email and TEQSA resource listings.

CQUniversity guide PDF Guide page 17 Guide page 12 GenAI for Educators email TEQSA assessment listing TEQSA student listings

Student impact and implementation records

First-year evidence, educator observations, result histories and cross-institutional context.

First-year survey screenshot First-year educator feedback Cross-institutional record Tutor feedback COIT20268 history COIT12212 history COIT20263 history COIT20248 history Interactive first-year dashboard Systems-analysis dataset

Independent, international, research and industry corroboration

Cross-disciplinary uptake, international approach/support, external case study, graduate outcome and industry review.

Business Ethics uptake email Initial international approach International support letter Warwick case study Graduate conferral letter Industry review

Scholarly reach and open-resource uptake

Dated publisher, Google Scholar and Zenodo captures.

ICT study publisher metrics Discover Education metrics Systematic review metrics Google Scholar counts Zenodo version 3 uptake Complete SAGE publication record

Practice publications and media records

Sector-facing translation and public communication already catalogued on the main publication page.

Future Campus The Educator CQUniversity News Gladstone Today SciEnMag Times Higher Education, HERDSA and full list

Supporting Material 2 (SM2) · Program synthesis

Three-minute SAGE Program Overview

This three-minute video provides a concise overview of the program’s development, student impact and wider adoption. The detailed evidence supporting these claims is presented throughout this page.

Duration: 2 minutes 58 seconds. The video is embedded here for convenience and is also available as a standalone supporting material: open SM2 directly ↗. The publications and documentary records on SM1 remain the detailed evidence base.

Evidence supporting the 2026 AAUT Program Award nomination