2025: ASCILITE 2025 Conference Proceedings

Assessment classification plays a critical role in ensuring transparency, coherence, and academic integrity across higher education curricula. The rapid emergence of generative artificial intelligence (genAI) has intensified the need for

Assessment classification plays a critical role in ensuring transparency, coherence, and academic integrity across higher education curricula. The rapid emergence of generative artificial intelligence (genAI) has intensified the need for institutions to assure learning outcomes that are demonstrably independent of AI-generated content. This challenge is compounded by the need to manage institutional risks, particularly misconduct arising from inappropriate genAI use, and to uphold assessment validity as a measure of student learning. Despite this urgency, there remains a lack of structured, scalable frameworks that support both pedagogical intent and institutional governance in assessment classification. This paper addresses that gap by proposing a revised classification system, comprising assessment categories and types, designed to meet the diverse needs of students, educators, and governance bodies. The model aims to enhance clarity, support constructive alignment, and enable data-informed decision-making across the curriculum. Drawing on TEQSA’s genAI principles and the Australian Qualifications Framework (AQF), the approach integrates verb-based classification aligned with learning outcomes to support a course-level approach to assessment. While implementation is ongoing, the proposed model offers a practical, standards-aligned response to institutional and regulatory challenges, contributing a framework that supports a pedagogically sound assessment while strengthening institutional capacity to assess and mitigate risk.

Southern Cross University (SCU) has adopted a proactive, educative, and iterative approach to integrating Generative Artificial Intelligence (GenAI) into learning and assessment. Recognising the limitations of early binary models, SCU

Southern Cross University (SCU) has adopted a proactive, educative, and iterative approach to integrating Generative Artificial Intelligence (GenAI) into learning and assessment. Recognising the limitations of early binary models, SCU developed and trialled increasingly nuanced frameworks, culminating in a practical and pedagogically aligned three-level model. This model categorises assessments based on permitted GenAI use and is embedded into formal assessment infrastructure, offering clarity for students and staff while promoting ethical and context-sensitive engagement. Supporting materials, including descriptor guidelines and task-specific use cases, were co-developed with educators and students and informed by empirical research on student responses to GenAI use. This paper outlines the policy journey, highlighting how SCU’s model balances institutional integrity, academic innovation, and sector guidance.

Curriculum mapping is a critical component of accreditation and continuous improvement in education. However, aligning Course Learning Outcomes (CLOs) to professional standards such as Engineers Australia’s Stage 1 Competency Standard

Curriculum mapping is a critical component of accreditation and continuous improvement in education. However, aligning Course Learning Outcomes (CLOs) to professional standards such as Engineers Australia’s Stage 1 Competency Standard remains time-consuming and labour-intensive. This study evaluates the potential of generative AI (GenAI) to support curriculum mapping by comparing automated outputs with expert human judgement. A stratified sample of 141 (10%) first-year CLOs from a national dataset was analysed using both manual review and GenAI (ChatGPT 4o). Each CLO was mapped to the 16 EA Stage 1 Competencies, and outcomes were classified as Match, Manual Only, GenAI Only, or Neither. Overall agreement was 81.2% with particularly strong alignment in professional and personal attributes. Mismatches were most common in technical competencies, where GenAI over-mapped based on keywords or under-mapped due to limited contextual understanding. These findings suggest GenAI can support curriculum review at scale but requires expert oversight for nuanced or discipline-specific outcomes. The work in progress study contributes to the growing literature on AI in education and offers practical insights into hybrid approaches for accreditation and curriculum design.

Simulation-based learning (SBL) offers students effective learning opportunities to practice and develop clinical skills but can be challenging to maintain long-term. For this study, we developed authentic video simulations requiring

Simulation-based learning (SBL) offers students effective learning opportunities to practice and develop clinical skills but can be challenging to maintain long-term. For this study, we developed authentic video simulations requiring clinical decision-making regarding appropriate antibiotic selection, to enrich the learning experience for students; and evaluated their impact on student learning and satisfaction. Two scenarios (tuberculosis and polymicrobial infection) were developed with expert input and filmed using professional actors and a small film crew. Between 2019 and 2024, second-year pharmacy students participated in SBL activities utilising the videoed scenarios. Evaluation was via pre- and post-tutorial questionnaires. Over a five-year period, pre- and post-activity questionnaires were completed by 233 students (62.5%) for tuberculosis and 275 (54.9%) for polymicrobial infection. Statistically significant differences between pre- and post-tutorial questionnaire scores were observed. Most students reported the SBL activities were outstanding/excellent (80.0-93.6%) and helped them to acquire critical thinking skills (mean: 90.1% tuberculosis; 93.2% polymicrobial infection). Positive outcomes were consistent across the five-year timeframe. SBL activities involving video simulations were a sustainable approach to enhancing students’ learning experience, and supported consolidation of knowledge about antimicrobial agents and practice of clinical decision-making skills in selecting appropriate antibiotics to treat infectious diseases.

This paper presents a new take on constructive alignment that follows an active, conversationalist approach to curriculum design. It introduces a practical tool that can be used iteratively to achieve constructive alignment. Ensuring

This paper presents a new take on constructive alignment that follows an active, conversationalist approach to curriculum design. It introduces a practical tool that can be used iteratively to achieve constructive alignment. Ensuring constructive alignment is an important yet complex task, which often educators find challenging to master due to time and the complexities involved. The simplicity of the proposed tool addresses this and has considerable potential to be applied at scale across the higher education sector to develop new curriculum and to facilitate continuous improvement of existing curricula. By repositioning the educator to be an integral part of constructive alignment, it recognises that while constructing alignment, the educator is in fact constructing a deeper understanding of what the learning narrative can be. Understanding the synergies of various elements interacting in a course will not only empower the educator to develop meaningful learning experiences but also allow them to help learners understand similar connections. It also presents a tremendous opportunity for technology companies to streamline their grading tools so that the attainment of individual outcomes necessary to satisfy constructive alignment principles can be explicitly and automatically captured.

The Project-work Artificial Intelligence Integration Framework (PAIIF) was developed to guide educators in embedding AI tools into project-based learning in engineering education. Grounded in the CDIO model, PAIIF extends the traditional

The Project-work Artificial Intelligence Integration Framework (PAIIF) was developed to guide educators in embedding AI tools into project-based learning in engineering education. Grounded in the CDIO model, PAIIF extends the traditional four project stages (Conceive, Design, Implement, Operate) by incorporating four additional sub-stages that emphasise evaluation, communication, and reflection. This paper presents the early application of the PAIIF across six undergraduate and one postgraduate engineering course at five Australian universities during 2024. To evaluate the framework’s effectiveness, a two-phase survey was deployed to capture students’ understanding and usage of GenAI tools before and after the integration. The implementation was tailored to each institution’s policies and curricular context, with instructors selecting relevant stages of the framework based on course learning objectives. This paper offers a snapshot of how GenAI tools were introduced to support ideation, documentation, and evaluation tasks within project work. While full survey results are still being analysed, early observations suggest that PAIIF provided a valuable scaffold for ethical and pedagogically aligned GenAI use. This work contributes initial practical insights for educators seeking to apply structured AI integration in engineering education and lays the foundation for future large-scale evaluations.

In the era of generative AI, prompting has become a ubiquitous, often invisible pedagogical act. As educators and students increasingly rely on gen-AI systems, prompting functions as a primary interface through which knowledge is sought

In the era of generative AI, prompting has become a ubiquitous, often invisible pedagogical act. As educators and students increasingly rely on gen-AI systems, prompting functions as a primary interface through which knowledge is sought, structured, and legitimised. This shift reconfigures pedagogical authority, agency, and epistemic legitimacy often in ways that reinforce epistemic injustice. Grounded in critical and postcolonial theories, the paper examines how prompting mediates access to knowledge and reinforces existing systemic inequalities. We argue that pedagogical interactions with gen-AI become a form of epistemic gatekeeping, privileging certain forms of language, logic, cultural and ontological norms. We articulate our position that the wholesale adoption of gen-AI demands a critical reckoning with its epistemic consequences, particularly the ways it automates and obscures inherited hegemonic and colonial knowledges. We call for a decolonial approach to gen-AI in curricula, beginning with the relational work of deep listening to the scholars and communities who have long imagined otherwise, and a commitment to act on what they have already asked for. We challenge higher education to resist the seductive efficiencies of gen-AI and confront the epistemological stakes of its choices.

The growing shift to digital education environments has accelerated the need for inclusive, flexible, and pedagogically sound learning strategies. Authentic e-learning, when paired with virtual site visits (VSVs), offers learners

The growing shift to digital education environments has accelerated the need for inclusive, flexible, and pedagogically sound learning strategies. Authentic e-learning, when paired with virtual site visits (VSVs), offers learners opportunities to engage with complex, real-world contexts in ways that are both accessible and equitable. This paper explores how VSVs can support authentic learning goals by eliminating traditional barriers associated with physical site visits, using the example of a detailed virtual simulation of the mechanical plant room in the Alan Gilbert Building at the University of Melbourne. Drawing from constructivist, experiential, and situated learning theories, this case study illustrates how virtual environments can foster deep learning and skill development. It also outlines inclusive design practices, challenges in implementation, and strategies for promoting equity. Through a case study and practical design experience, the paper proposes that inclusive VSVs are not just a temporary substitute, but a lasting innovation for 21st century education.

Preparing future engineers requires moving beyond traditional, siloed teaching methods toward approaches that build adaptability, resilience, interdisciplinary collaboration, and development of a robust engineering identity. However, many

Preparing future engineers requires moving beyond traditional, siloed teaching methods toward approaches that build adaptability, resilience, interdisciplinary collaboration, and development of a robust engineering identity. However, many first-year engineering courses still emphasise theoretical content over practical, real-world skills. In response, this paper presents a food delivery robotics project co-developed by the School of Electrical Engineering and Telecommunications and UNSW Makerspace as a case study in curriculum design and innovation. The project uses hands-on learning, iterative skill-building, and a modular arena to engage students in authentic engineering challenges. By embedding experimentation, failure, and teamwork into the learning process, the initiative fosters a growth mindset and mirrors the collaborative nature of the engineering profession. This paper outlines the challenges and solutions involved in integrating Makerspace practices into the curriculum and highlights how such approaches can better prepare students for the evolving demands of the profession. The success of this initiative underscores the potential of interdisciplinary, practice-based education in cultivating the next generation of agile, industry-ready engineers.

This paper presents RMIT University's Digital Learning Environments Innovation Engine (DLEIE) a strategic, experiment-driven framework for continuous and scalable innovation in digital learning. Conceived as a post-COVID initiative to

This paper presents RMIT University's Digital Learning Environments Innovation Engine (DLEIE) a strategic, experiment-driven framework for continuous and scalable innovation in digital learning. Conceived as a post-COVID initiative to create a vibrant Digital Campus, the DLEIE evolved from RMIT's earlier Digital Campus conceptualisation. It addresses systemic challenges in digital learning experiences faced by large, multi-campus institutions with diverse learners, siloed governance, and complex technological ecosystems which affect these organisations to respond in agility. The DLEIE emphasises low-risk experimentation, institutional ownership, and rapid, user-informed iteration cycles. In 2024, five targeted experiments addressed student engagement, wellbeing, personalisation, and usability within Canvas LMS. These interventions including academic calendar overlays, wellbeing nudges, course navigation redesign, digital identity tools, and micro-feedback mechanisms tackled longstanding gaps in digital learning design. The 2025 strategy expands through cross-disciplinary focus groups, qualitative interviews, and co-design hackathons involving students, staff, and industry partners. We examine early outcomes, lessons learned, and cultural and structural enablers necessary to embed agile innovation in higher education. The DLEIE offers a replicable model for universities seeking to move beyond vendor-led digital change toward more responsive, inclusive, and effective digital learning environments.

This concise paper reports on a work-in-progress research project that involves the design, development and testing of learning analytics dashboards (LADs) designed to actively promote students’ and educators’ agency. A set of design

This concise paper reports on a work-in-progress research project that involves the design, development and testing of learning analytics dashboards (LADs) designed to actively promote students’ and educators’ agency. A set of design principles were developed through analysis of literature on student-facing LADs that related to principles of inclusivity, transparency, explainability and agency. This set of principles informed the design and development of a prototype simulated LAD used for research purposes with university students from equity and non-equity groups. This paper explains and illustrates how these design principles were translated into design features that empower students to take control of their learning and build their data literacy. This work addresses a key shortcoming of most current dashboards, highlighting the need to develop LAD designs that address the nexus between learner inclusivity, equity and agency.

Developing and maintaining student relationships are crucial in tertiary education because they impact engagement, retention, and learning. The Student Relationship and Engagement System (SRES), developed at the University of Sydney, is a

Developing and maintaining student relationships are crucial in tertiary education because they impact engagement, retention, and learning. The Student Relationship and Engagement System (SRES), developed at the University of Sydney, is a tool that integrates within a legacy learning management system and enables customised communications with students based on criteria such as attendance, assessment performance, and login frequency. This study utilised interviews from N = 25 SRES users across Curtin University faculties to explore its effectiveness and challenges, as well as what features could be added to SRES to improve its usefulness. Findings revealed that the initial usage focused routine and efficiency-driven tasks. However, advanced users leveraged the tool for more complex and adaptive uses, particularly in clinical or practical settings. While less experienced users found SRES beneficial primarily in large classes, more experienced users valued it irrespective of class size when linked to assessments and learning. Future enhancements suggested by participants included integrating artificial intelligence for generating communications or criteria automation, indicating potential advancements for SRES's utility.

Generative AI (GenAI) offers transformative potential in education, yet the real-world adoption of (Gen)AI in education remains slow and uneven. This study aims to validate a new instrument measuring educators’ perceived cognitive and

Generative AI (GenAI) offers transformative potential in education, yet the real-world adoption of (Gen)AI in education remains slow and uneven. This study aims to validate a new instrument measuring educators’ perceived cognitive and socio-affective trust in GenAI as a collaborator and to investigate if this trust varies across teaching levels, experience and academic qualifications. Inferential analyses such as One-Way ANOVA, nested ANOVA and General Linear Modelling (GLM) were conducted on self-reported data from 212 educators in Singapore. The findings revealed that educators with doctoral degrees in professional/adult education demonstrated the highest cognitive and socio-affective trust, while educators at the primary education level reported the lowest. These findings challenge previous research that reported no significant demographic or professional differences in trust in AI, highlighting the need for designing GenAI and its adoption strategies that are tailored to educators’ professional context and training. By identifying educators’ trust as a key factor in GenAI adoption, this study advances a human-centred perspective on trust in GenAI, particularly within the context of human-AI collaboration in teaching practice.