Practical Implications of Generative AI on Assessment: Snapshot of Early Reactions to Assessment Redesign in an HRM and a Psychology Course

This study investigated the impact of generative AI usage on academic engagement among students at a selected College of Education in Ghana. The study aimed to examine the kinds of generative AI tools, identify the different ways students utilize these GenAI in their learning, and assess the overall influence on their academic engagement by employing a sequential explanatory mixed-methods design. This design was chosen to provide a comprehensive understanding through both quantitative and qualitative data. Ninety-four students participated in the quantitative phase via purposive sampling and completed a survey examining the types of generative AI tools they use and the effects on their academic engagement. Additionally, twelve students were interviewed to gather in-depth qualitative insights that could not be captured by the survey. Findings Revealed that generative AI positively influences students’ academic engagement and improves their learning environment. It serves as an effective tool to enhance learning and engagement. However, findings from some respondents via qualitative interview reveal that, excessive reliance on generative AI also poses risks by encouraging laziness and overdependence, less creativity and immersive engagement due to easy access to the AI tools, which may affect academic integrity. The implication for this study is that generative AI tools like ChatGPT spark curiosity by offering instant feedback, tailored learning journeys, and interactive experiences that turn complex concepts into manageable insights. The study highlights generative AI as a double-edged tool: while it empowers students with efficiency, creativity, and deeper engagement, it also risks encouraging shortcuts, dependency, and ethical breaches. Ensuring responsible and ethical integration of AI is therefore vital, with academic integrity anchored in fairness, honesty, and originality remaining at the heart of scholarly practice.

Generative AI (GenAI) is penetrating all areas of life, including higher education, where teachers and examiners are debating how to respond. While there are legitimate concerns about its potential negative impact on academic integrity, there are also learning and efficiency benefits that this technological advancement brings (Crompton and Burke, 2023; Abdaljaleel et al., 2024; Bond et al., 2024). We must develop a workforce that can confidently, effectively, efficiently, and responsibly use technological advancements, including GenAI. Our study relates to a GenAI-related activity in a postgraduate Finance course. It explored the role GenAI played when students worked in groups and how it affected their interaction, engagement, learning, efficiency, and confidence. Does GenAI enhance or hinder group work, which is an essential graduate skill? Moreover, how do students perceive the role of GenAI in enabling them to engage with the task, complete it better, and interact with group members? While the ability to use GenAI effectively might be an important graduate skill, it is crucial to train students to become responsible citizens who use this technology ethically. In our presentation, we described how the activity was conducted and shared our findings, including how many students used GenAI when allowed, what they used it for, the types of GenAI used, how effectively prompts were used, what students learnt, and how GenAI affected their interaction and engagement in the task. Some insights about the benefits and challenges of GenAI would be obtained from this study. The design of our practical study in the classroom and the students’ experiences and perceptions about GenAI will be useful to the teaching community and policy makers in higher education. Moreover, the insight into the use of GenAI in the classroom will also be useful. Such an activity can be replicated and tailored to suit different classes.

Generative AI (GenAI) tools such as ChatGPT, Gemini and Copilot have created concerns in academia, particularly after the launch of ChatGPT. GenAI and AI have been the buzz words and academics are discussing about the possibilities of its positive and negative impacts on educations and research. Recently, studies have been conducted on the influence of GenAI tools in education and research. With the above concerns and the impact of GenAI, grounded on Vygotsky's Zone of Proximal Development (ZPD) as a theoretical lens, this study explores how English language teachers integrate GenAI tools to enhance teaching and learning. Particularly, this study explores the integration of GenAI tools in English language teaching and learning, focusing on teaching efficiency, student engagement, personalized learning, and writing skills, subscribing to exploratory research methods grounded on semi-structured interviews. The findings of the study affirmed the positive impact of GenAI tools on teaching efficiency, students’ engagement, and writing skills. The results indicated that GenAI positively influences teaching efficiency and student engagement in learning. The implications of this research highlighted the potential of GenAI tools to create a more intelligent and personalized learning environment for English language teaching that benefits both educators and learners.

Generative AI (GenAI) technologies have been widely adopted by college students since the launch of ChatGPT in late 2022. While the debate about GenAI’s role in higher education continues, there is a lack of empirical evidence regarding whether and when these technologies can improve the learning experience for college students. This study utilizes data from a survey of 72,615 undergraduate students across 25 universities and colleges in China to explore the relationships between GenAI use and student learning engagement in different learning environments. The findings reveal that over sixty percent of Chinese college students use GenAI technologies in Academic Year 2023–2024, with academic use exceeding daily use. GenAI use in academic tasks is related to more cognitive and emotional engagement, though it may also reduce active learning activities and learning motivation. Furthermore, this study highlights that the role of GenAI varies across learning environments. The positive associations of GenAI and student engagement are most prominent for students in “high-challenge and high-support” learning contexts, while GenAI use is mostly negatively associated with student engagement in “low-challenge, high-support” courses. These findings suggest that while GenAI plays a valuable role in the learning process for college students, its effectiveness is fundamentally conditioned by the instructional design of human teachers.

Despite concerns about students’ use of generative AI (GenAI) in assessment, the technology has become embedded into students’ everyday assessment practices. It is unclear how students are making judgements about their ways of working with GenAI and what impact this has upon their learning. This qualitative multimodal study examines students exercising judgement as they work with GenAI to complete assessment tasks. Twenty-six interviews were conducted with Australian university students, primarily using a scroll-back approach, which revisits traces of students’ historical interactions with GenAI in the interviews. Employing a holistic definition of judgement and a narrative approach to analysis, we interpreted six distinct categories of judgement events. These are: 1) making judgements about knowledge when working with GenAI; 2) learning to judge GenAI through its limitations; 3) relying on GenAI for things they could not otherwise do; 4) adopting ideas with low levels of criticality; 5) misjudging GenAI contributions as their own; and 6) submitting GenAI content in an assignment without judging it. This study suggests GenAI use strongly shapes student learning in complex ways when undertaking assessment tasks, and that making judgements about GenAI entails a student making judgements about their own knowledge, deficits, and quality of contributions.

This study examined the guidelines issued by the top 50 U.S. universities regarding the use of Generative AI (GenAI) in academic and administrative activities. Employing a mixed methods approach, the research combined topic modeling, sentiment analysis, and qualitative thematic analysis to provide a comprehensive understanding of institutional responses to GenAI. Topic modeling identified four core topics: Integration of GenAI in Learning and Assessment, GenAI in Visual and Multimodal Media, Security and Ethical Considerations in GenAI, and GenAI in Academic Integrity. These themes were further explored through sentiment analysis, which revealed highly positive attitudes towards GenAI across all institution types, with significant differences between faculty and student-targeted guidelines. Qualitative thematic analysis corroborated these findings and provided deeper insights, revealing that 94% of universities had faculty guidelines emphasizing the importance of establishing and communicating course-specific GenAI policies. This analysis also highlighted recurring themes such as academic integrity and privacy concerns, which aligned with the security and ethical considerations identified in the topic modeling. The study highlights the rapid evolution of GenAI guidelines in higher education and the need for flexible, stakeholder-specific policies that address both the opportunities and challenges presented by this technology.

The launch of OpenAI’s ChatGPT model in late 2022, as most Australian universities wound down for summer holidays, elicited varied responses from higher education practitioners, policy makers and commentators that ranged from heightened concern and proscriptive impulses through to cautious excitement about the potentially disruptive, deceptive impact of university student use of AI chatbots (Skeat and Ziebell, 2023).
 
 Generative AI has both transformative and disruptive implications for conventional university assessment practices. Simultaneously, we observed a tension between university teaching and learning imperatives of digital literacy, academic integrity, student employability, and data security and privacy.
 Large Language Models (LLMs) run on deep learning programming, trained to process data in a way modelled on human brain cognition, to generate human-like responses to natural language prompts. Generative AI can answer and compose questions, write narratives, summarise documents, and construct essays, reports, reviews etc, and perform reflective writing capabilities (Li et al., 2023). Importantly, generative AI performs these tasks with substantially different degrees of accuracy, biases, and relevance potentially with each prompt.
 
 These dynamic and iterative learning abilities have significantly, sometimes imperceptibly, compromised the integrity and reliability of conventional university assessment types. Moreover, generative AI is improving incrementally, increasingly integrated into everyday software, platforms and apps (Liu and Bridgeman, June 2023). Nor is it only traditional written assessments that are at risk of disruption and invalidation. AI image generators like OpenAI’s DALL-E can produce high-quality, realistic and fantastical artworks.
 
 ChatGPT-like AI models are designed for conversational and dialogic user experiences, programmed on natural and intuitive patterns of language use. Even without any targeted training in ethical, effective and critical ‘prompt engineering’ (cf. Liu, 2023) students can output passable assessment content.
 
 As well as concerns around digital literacy, academic integrity and meaningful learning, prompting, performed rudimentarily at least, blurs the lines between a student’s original thinking (and integration of sources) and machine-generated output. The foundational challenge being in determining whether a student's submission is a result of their applied understanding or the AI's algorithmic capabilities. Yet, this GenAI interactional, iterative user experience can also be harnessed by educators to design, facilitate and assess socially constructivist, authentic, analytical, and innovative approaches to student learning (Liu and Bridgeman, June 2023).
 
 We report on a research project that implemented an iterative, nested, and collaborative assessment redesign (Lodge et al. 2023) as an alternative to a 2000-word Final Research Report due in the semester’s penultimate week. For the redesign, we partially broke the one submission down into three, smaller critical reflections due across a semester. For the first, students used ChatGPT before, and then after, learning a prompt engineering approach (cf. Liu, 2023). Secondly, students reflected on their engagement with generative AI as a collaborator in comparison to their collaboration with peers on a task. The final critical reflection required students to anticipate how generative AI might impact their professional practices drawing on the subject’s key topics.
 
 With ethics approval granted, our research findings are drawn from the roughly 10% of all students (n = 83) that chose the redesigned option. We analyse their three submissions in terms of existing themes in the literature (cf. Skeat and Ziebell, 2023) around academic integrity, digital literacy, institutional messaging and student belonging, and generative AI as ‘study buddy’ (Skeat and Ziebell, 2023).

With the rapid evolution and widespread adoption of Generative AI (GenAI), there has been a recent surge in research on students’ perceptions and usage of these tools for learning. However, a critical need remains to understand how students from various demographic groups perceive and utilize GenAI for educational purposes. This research aims to fill this gap by evaluating students’ use of GenAI, comfort level, perception of readiness, benefits and challenges, and demographic differences in their perceptions. Through an online survey of 482 students across diverse institutions of higher education primarily within the United States, the findings indicate a significant majority of students feel comfortable using GenAI tools and recognize their potential to enhance productivity and academic success. However, students also reported concerns related to GenAI use, including concerns about academic integrity, overreliance on AI, and data privacy and security. The analysis highlights differences in the subscales of perceived readiness (GenAI Comprehension; GenAI Utilization and Proficiency), benefits (Impact of GenAI; GenAI Empowerment), and challenges (Negative Impact of GenAI; GenAI Limitation) across demographic groups, including student classification, enrollment status, and institution types. Findings suggest a critical need for the integration of AI literacy into curricula to address opportunities and challenges posed by GenAI in higher education, and this study calls for additional research to explore evolving perceptions of GenAI use over time and its long-term impact on higher education.

The emergence of Generative AI (GenAI) in education marks a transformative shift in teaching, learning, and assessment practices. GenAI technologies have evolved from being simple conversational agents to highly advanced systems capable of generating human-like text, solving complex problems, assisting in creative writing, tutoring, and even providing personalized feedback to students. This paper explored the use of GenAI in education with emphasis on its implications towards academic integrity. Information and Communications Technology (ICT) college students (n=30) were purposively sampled to be interviewed regarding their experiences and perceptions about the use of GenAI in their program. Thematic analysis was carried out to identify emerging themes and assess the implications of GenAI use in education, particularly ICT learning processes. Students believed that GenAI offers significant benefits, such as personalized learning, assistance in creative writing, tutoring, coding, and grading, which enhance students’ learning experiences by providing instant feedback and helping them understand difficult concepts. Concerns about over-reliance on AI, plagiarism, and academic dishonesty have emerged, as some students use AI tools to bypass intellectual effort, raising questions about the authenticity of their work. ICT students expressed concerns about GenAI fostering cheating and undermining academic independence. Despite these issues, students remain optimistic about GenAI’s role in education, advocating for clear guidelines on its appropriate use to balance its benefits with the need for academic integrity. The integration of GenAI in educational settings necessitates careful management to ensure that it supports, rather than diminishes, the learning process.

Generative AI (GAI) is reshaping the future of work in architecture by introducing innovative ways for humans to interact with technology, transforming the design process. In education, GAI offers students immersive environments for iterative exploration, enabling them to visualize, refine, and present design concepts more effectively. This paper investigates how GAI, through a structured framework, can enhance the learning of design tasks in elaborating interior design proposals, and preparing students for the evolving professional landscape. Drawing on the platform Midjourney, students explored concepts, material moodboards, and spatial compositions, simulating professional scenarios. Each student was assigned a real client and tasked with developing tailored design solutions, guided by client and tutor feedback. This approach demonstrates how GAI supports the development of future-oriented skills, directly linking education to the technological shifts in professional practice (Araya, 2019). The study adopts a practice-based methodology, documenting the outcomes of an interior design workshop where students employed GAI tools to develop client-specific proposals. Students engaged in role-playing, meeting their assigned clients face-to-face to gather requirements, acting as junior architects. They analyzed client feedback to inform the design phase, after which they used a structured framework for better using GAI to iteratively refine their proposals. By generating AI-assisted visualizations of spatial configurations and materials, students developed final design solutions that aligned with client expectations. Data from GAI iterations, client feedback, and tutor evaluations were used to assess how effectively AI tools contributed to producing professional-quality designs (Schwartz et al., 2022). Two research questions frame this investigation: (1) How does Generative AI enhance students' ability to create client-specific interior design solutions, from concept generation to final visualization, within a structured educational framework? (2) How does the integration of GAI tools impact the teaching of iterative design processes in architecture, particularly in preparing students for the future of work in the profession? The findings reveal that GAI significantly improved students' design outcomes by enabling them to visualize and refine their proposals based on real-world scenarios. GAI facilitated the exploration of current trends and supported the creation of material moodboards and space visualizations. The iterative nature of AI tools allowed students to better grasp the relationships between spatial configurations, design choices, and client needs. Their final proposals, incorporating AI-generated outputs, were praised for their conceptual clarity and technical precision, reflecting how AI-driven processes can transform traditional workflows (Burry, 2016). This study illustrates the transformative potential of GAI in architectural education, particularly in fostering dynamic human-technology interactions. By leveraging AI, students maintained control over outputs while transforming abstract concepts into client-ready designs. Moreover, the iterative feedback loop enabled by GAI promoted a more adaptive and responsive learning process, giving students real-time insights into their design decisions. These insights reflect broader changes in the future of work, where AI-driven tools will become integral to professional practice. Future research could explore expanding GAI’s role in more complex design stages, such as schematic design and development, building on the benefits observed in this study.

Generative AI (hereinafter GenAI) technology, such as ChatGPT, is already influencing the higher education sector. In this work, we focused on the impact of GenAI on the academic integrity of assessments within higher education institutions, as GenAI can be used to circumvent assessment approaches within the sector, compromising their quality. The purpose of our research was threefold: first, to determine the extent to which the use of GenAI can be detected via the marking and moderation process; second, to understand whether the presence of GenAI affects the marking process; and finally, to establish whether authentic assessments can safeguard academic integrity. We used a series of experiments in the context of two UK‐based universities to examine these issues. Our findings indicate that markers, in general, are not able to distinguish assessments that have had GenAI input from assessments that did not, even though the presence of GenAI affects the way markers approach the marking process. Our findings also suggest that the level of authenticity in an assessment has no impact on the ability to safeguard against or detect GenAI usage in assessment creation. In conclusion, we suggest that current approaches to assessments in higher education are susceptible to GenAI manipulation and that the higher education sector cannot rely on authentic assessments alone to control the impact of GenAI on academic integrity. Thus, we recommend giving more critical attention to assessment design and placing more emphasis on assessments that rely on social experiential learning and are performative rather than output‐based and asynchronously written. Practitioner notesWhat is already known about this topic GenAI has enabled students to complete higher education assessments quickly and with good quality, leading to challenges in academic integrity. GenAI has transformed the requirements and considerations in assessment design in higher education. Authentic assessments are seen as a prominent way to tackle the GenAI challenge. What this paper adds We provide quantitative and qualitative experimental evidence suggesting that GenAI can generate authentic assessments that pass the scrutiny of experienced academics. We demonstrate how the use of authentic assessments alone does not protect the academic integrity of students in higher education. Our qualitative analysis indicates that markers may generate false positive and false negative results if they suspect GenAI tampering in an assessment. Thus, students' learning is not assessed correctly. Implications for practice and/or policy When universities and national organisations design policies regarding GenAI, authentic assessments are not the panacea; the focus must remain on assessment design. Assessments of learning need to shift from assessing output to focusing on process and relevance to the workplace. That would mean a paradigmatic shift from written assessments to synchronous interpersonal assessments. The move away from written assessments has implications that are far reaching for the academy if written assessments cannot be trusted as a reliable indicator for and of learning.

Research shows that feedback practices significantly impact key student outcomes, including performance, engagement, and satisfaction (Esmaeeli, Shandiz, Shojaei, Fazli & Ahmadi 2023). Feedback is a crucial component of learning in Higher Education (HE) and plays a vital role in developing critical thinking, improving retention, and enhancing student engagement. The importance of timely dialogic feedback in enhancing student engagement and potentially improving retention is well understood (Advance HE 2020). However, academic staff are increasingly time-poor, with reduced opportunities to provide regular in-depth quality feedback outside of that given for summative assessment (Henderson, Ryan & Phillips 2019). Early experimentations with using Generative AI (GenAI) such as ChatGPT to provide feedback for formative assessment recognises that students will learn and work in an AI-enabled world beyond their university studies (Bowditch 2023). GenAI can be leveraged inside and outside the classroom to achieve positive student engagement and improved skill development thereby affording them the skills and knowledge necessary to succeed (Hooda et al. 2022). Engaging with GenAI for feedback purposes offers an opportunity to increase equitable access to feedback across the student cohort, to support and further develop their critical skills and learning outcomes. As Verhoeven and Rana (2023) note, “AI disruption may present an opportunity to shift the focus from assessment of learning to assessment for learning”. Utilising GenAI for feedback purposes can provide rapid, personalised learning support, and aid with planning, drafting, and revising student work. However, this adoption of GenAI for feedback must be driven and developed by the educator, keeping the human in the loop to ensure quality (Atchley, Pannell, Wofford, Hopkins & Atchley). Our project draws on the principles of feedback literacy, current research on using AI as learning tool (Verhoeven & Rana 2023b; Tubino & Adachi 2022) and emphasises student-centred learning through dialogic feedback practices. The project draws on scholarship from Mollick and Mollick’s seven approaches to student use of AI (2023), Perkins, Furze, Roe and MacVaugh’s framework for ethical integration of AI in assessment (2024), and emerging work from Liu, Brightman, and Miller on GenAI and feedback (2023). This presentation addresses the conference theme of Technology, providing an overview and reflection on staff development and adoption of GenAI for feedback processes for the benefit of student learning. We will showcase four use cases of the use of GenAI to design and implement feedback creation for undergraduate formative assessment across the three Colleges at the University of Newcastle. All cases engage innovation in Technology Enhanced Learning (TEL) practice in developing GenAI tools to support student learning via feedback. The presentation addresses the benefits and challenges of each approach. Recognising the value of feedback in student learning, this PechaKucha is aimed at a diverse audience in HE. Our presentation will demonstrate applicability and adaptability to a range of disciplines as we explore the impact new and emerging GenAI technologies can have on HE. We will introduce the possibilities of using GenAI for feedback purposes, and encourage staff to consider experimenting with and adopting their own innovative TEL practices.

There is a well-established body of research on effective feedback practices in higher education. Yet, student engagement with feedback remains varied. One possible explanation lies in limited feedback literacy, particularly the difficulty students face in managing the affective dimensions of receiving critique. Emotional responses can inhibit students from acting on lecturer feedback. This raises the question: can removing the relational aspect of feedback improve engagement? Generative AI (GenAI) tools are increasingly embedded in various educational contexts, supporting both students and teachers by offering real-time feedback and facilitating assessment processes. However, integrating GenAI into feedback practices in higher education requires a paradigm shift, one that positions GenAI not as a threat or all-knowing authority, but as a collaborative learning partner with the potential to foster dialogic engagement with students. With this in mind, we adopted a Bakhtinian approach to dialogue to qualitatively explore whether AI-generated feedback can reshape students’ engagement by reducing affective barriers and legitimising AI as a dialogic partner in learning. Drawing on Bakhtin’s concepts of authoritative and internally persuasive discourse, we investigate whether interaction with GenAI feedback encourages greater student agency and engagement with the feedback process among students from widening participation backgrounds at a London post-1992 university.

This study evaluates the efficiency and accuracy of Generative AI (GAI) tools, specifically ChatGPT and Gemini, in comparison with traditional academic databases for industrial engineering research. It was conducted in two phases. First, a survey was administered to 101 students to assess their familiarity with GAIs and the most commonly used tools in their academic field. Second, an assessment of the quality of the information provided by GAIs was carried out, in which 11 industrial engineering professors participated as evaluators. The study focuses on the query process, response times, and information accuracy, using a structured methodology that includes predefined prompts, expert validation, and statistical analysis. A comparative assessment was conducted through standardized search workflows developed using the Bizagi tool, ensuring consistency in the evaluation of both approaches. Results demonstrate that GAIs significantly reduce query response times compared to conventional databases, although the accuracy and completeness of responses require careful validation. A Chi-Square analysis was performed to statistically assess accuracy differences, revealing no significant disparities between the two AI tools. While GAIs offer efficiency advantages, conventional databases remain essential for in-depth literature searches requiring high levels of precision. These findings highlight the potential and limitations of GAIs in academic research, providing insights into their optimal application in industrial engineering education.

Thus, the appearance of the Generative Artificial Intelligence opened up a great turn in many areas, including education and creative industries. This paper seeks to understand the deep impact that Generative AI is going to have on learning and creating processes for the social context of Generation Z (Gen Z) students – born in digital culture. The work looks into the possibilities and challenges that Gen Z in collaboration with Generative AI leads to the future of learning and creativity. This paper is relevant as it offers some understanding of the ongoing changes in the education and creativity together with the escalating growth in technology. The nature of the association between the members of Generation Z and the Generative AI needs to be known by the educational stakeholders, policymakers, and business executives to leverage value from the existing and upcoming technologies together with dealing with possible negative impacts. The purpose of this study was to explore the nature and uses of Generative AI, and its effects on the learning and creativity of Gen Z, in addition to identifying the advantages, disadvantages, opportunities, and risks/partities’ concerns that are commensurate with the integration of this technology in teaching/learning and creative processes. To achieve the objectives of the study the following research methodology was used: The research used both a literature review and documentary research. The materials used included academic publications, Industry reports, books and other credible internet sources on Generative AI and its impact on the education and creativity of the Gen Z. The document analysis included policy papers, educational technology reports, case studies and white papers from academic and professional bodies as well as other industries that involve Generative AI. Several insights show that using Generative AI can positively impact learners’ experiences, engagement, and creativity. However, there was some controversy about the excessive usage of AI and claimed that because of it people may get worse at critical thinking. The following were noted to be major concerns; Ethical Issues: they included issues to do with bias in the algorithms as well as the right to privacy of data. Thus, the findings of this research point to a three-way settlement with respect to the use of Generative AI in education and creative industries. It underlines the guideline of how human creativity and critical thinking ought to be sustained, while using AI tools. Proposals include, the need to teach critical thinking alongside AI use, fostering ethical AI consciousness, surged AI education, appropriate non-ethnical AI data set, strong AI policies and pro positive AI inspires and creative constructive use. The research implications for future studies include studying the changes in the achievement of learning outcomes over a period of time, wherein Generative AI has been incorporated and understanding how this technology influences different learning styles and needs, the issues of ethical and privacy concern, the requirement of professional development to educators in relation to Generative AI and finally, the comparison information and communication technology for learning between different cultures. Related to that, further studies on the effectiveness of AI in approaches like collaborative learning, its potential on preparing learners for employment, and on the psychology of students would be helpful in informing the future advancement of Generative AI in school and particular creative areas.