The integration of artificial intelligence (AI) into higher education assessment for coding subjects has the potential to enhance student engagement and success. However, there are also concerns about the potential for AI to be used to facilitate academic dishonesty. This study explores the use of AI in assessment tasks in a higher education landscape, particularly when it comes to challenging subjects such as coding. It examines how AI-enhanced assessment tasks could help to increase student engagement and motivation, while also identifying areas where students need additional support. The study also discusses the challenges and opportunities associated with the use of AI in assessment, with a particular focus on academic integrity.
The findings of the study suggest that AI-enhanced assessment tasks can be a valuable tool for improving student learning in coding courses. However, careful planning and implementation are essential to ensure that AI is used effectively and ethically. Specifically, it is recommended that AI-enhanced assessment tasks be designed to be aligned with the learning objectives of the course and to be resistant to plagiarism and other forms of academic dishonesty. It is also recommended that students be given the opportunity to provide feedback on the AI- enhanced assessment tasks. This feedback can be used to improve the design of the tasks and to ensure that they are meeting the needs of the students.
These findings have the potential to guide the creation and application of AI-augmented assessment tasks across diverse higher education courses. Nevertheless, it’s important to acknowledge that the use of AI in assessments is a multifaceted matter lacking simple solutions. Both potential advantages and risks are linked with AI’s adoption, demanding careful deliberation of these aspects when deciding on its utilization.

Assessments support learning and frame how and what students learn. It also plays a central role in the certification of student nurses which is important to determine if  students have  the requisite knowledge and skills to become Registered Nurses. The inclusion of rubrics in assessments communicate expectations of assessments to students. A rubric can prompt students to address certain aspects of an assessment task they may not have thought to, and in this regard are a significant point of enactment. They also act as an invitation for students to work with the assessment criteria to understand how to complete the assessment task.

When preparing written assessment students are expected to undertake several actions to support their learning such as literature searching that uses nursing databases, reading published, current, peer reviewed evidence-based sources, paraphrasing this information, and attribute this work through correct citation practices.  However, these good academic behaviours are being challenged by the ease of access to a plethora of low-quality electronic information available via search engines such as Google, contract cheating services and more recently Generative AI. When students use these low-quality information sources to complete assessments their learning and potentially their integrity is jeopardised.

This project aims to espouse behaviours associated with academic success and integrity by rewarding them through the allocation of “ownership and integrity” marks in the assessment rubric. Marks were awarded for using robust literature searching methods, similarity index (from Turnitin) <15% and correct citation for at least 6 academic sources.

Katherine Gilliver-Brown – Senior Tutor/ University of Waikato
Rachel Fulton – Student Discipline Manager/ University of Waikato

In conjunction with Queensland University of Technology

In the face of changing higher education dynamics and advancing technology, traditional assessment methods are frequently questioned for their relevance and effectiveness. My aim was to innovate assessment design, foster student engagement, and importantly, deter AI-assisted cheating. I put this into practice in a large first-year course at ANU, transforming a typical 2500-word essay into a dynamic group multimedia project addressing environmental issues.

This innovative change was more than a shift in format; it was a strategy to cultivate vital 21st-century skills: critical thinking, collaboration, creativity, and communication. To boost team cohesion, I introduced a unique group-forming exercise – ’36 Questions to Fall in Love’ – which resulted in statistically significant improvements in feelings of connection and closeness among team members, particularly for female and minority group students.

The new assessment approach received overwhelmingly positive feedback from students, enriching their learning experience and improving team dynamics. This experience underscores the potential of such creative, collaborative assessment designs in promoting active learning while minimising opportunities for AI-aided dishonesty.

The practice invites further exploration and adaptation in diverse educational contexts and offers valuable lessons for educators aiming to maintain meaningful, relevant assessment methods resilient to academic integrity breaches in the digital age.

Sandy Noakes, Lynn Berry, Elen Seymour, Joan Lynch, Fidelis Mashiri, Zhao Zou
Western Sydney University

This poster presents preliminary findings from an applied research project designed to build staff and institutional capacity through recommendations, strategies and good practice examples of detection and reporting of academic misconduct. Findings are based on qualitative comments from an online survey of academic misconduct reporting practices in different text and non-text-based disciplines, in different schools within an Australian University. Findings are being examined with reference to a desktop audit of academic misconduct reporting processes, forms and staff resources; benchmarking against public information on processes and resources from other universities; and descriptions of activity in higher education standards and awards.
Survey participants included academic and professional staff involved in detecting, reporting and managing academic misconduct. Based on preliminary analysis, a framework has been developed to show the multi-layered and interdependent nature of influences on detecting and reporting activity to be considered when building individual and institutional capacity. Project outcomes will inform misconduct detection and reporting processes and procedures, to help close the loop on policy and practice. Outcomes will also inform the design and development of staff resources, alternative assessment tasks and student resources that support institutional compliance with TEQSA guidance and the Higher Education Standards Framework (Threshold Standards) 2021.