Massey Documents by Type
Permanent URI for this communityhttps://mro.massey.ac.nz/handle/10179/294
Browse
5 results
Search Results
Item Generative AI, Large Language Models, and ChatGPT in Construction Education, Training, and Practice(MDPI (Basel, Switzerland), 2025-03-15) Jelodar MB; Senouci AThe rapid advancement of generative AI, large language models (LLMs), and ChatGPT presents transformative opportunities for the construction industry. This study investigates their integration across education, training, and professional practice to address skill gaps and inefficiencies. While AI’s potential in construction has been highlighted, limited attention has been given to synchronising academic curricula, workforce development, and industry practices. This research seeks to fill that gap by evaluating AI adoption through a mixed and multi-stage methodology, including theoretical conceptualisation, case studies, content analysis and application of strategic frameworks such as scenario planning, SWOT analysis, and PESTEL frameworks. The findings show AI tools enhance foundational learning and critical thinking in education but often fail to develop job-ready skills. Training programmes improve task-specific competencies with immersive simulations and predictive analytics but neglect strategic leadership skills. Professional practice benefits from AI-driven resource optimisation and collaboration tools but faces barriers like regulatory and interoperability challenges. By aligning theoretical education with practical training and strategic professional development, this research highlights the potential to create a future-ready workforce. The study provides actionable recommendations for integrating AI across domains. These findings contribute to understanding AI’s transformative role in construction, offering a baseline for effective and responsible adoption.Item Chat2VIS: Generating Data Visualizations via Natural Language Using ChatGPT, Codex and GPT-3 Large Language Models(IEEE, 2023-05-08) Maddigan P; Susnjak T; Didimo WThe field of data visualisation has long aimed to devise solutions for generating visualisations directly from natural language text. Research in Natural Language Interfaces (NLIs) has contributed towards the development of such techniques. However, the implementation of workable NLIs has always been challenging due to the inherent ambiguity of natural language, as well as in consequence of unclear and poorly written user queries which pose problems for existing language models in discerning user intent. Instead of pursuing the usual path of developing new iterations of language models, this study uniquely proposes leveraging the advancements in pre-trained large language models (LLMs) such as ChatGPT and GPT-3 to convert free-form natural language directly into code for appropriate visualisations. This paper presents a novel system, Chat2VIS, which takes advantage of the capabilities of LLMs and demonstrates how, with effective prompt engineering, the complex problem of language understanding can be solved more efficiently, resulting in simpler and more accurate end-to-end solutions than prior approaches. Chat2VIS shows that LLMs together with the proposed prompts offer a reliable approach to rendering visualisations from natural language queries, even when queries are highly misspecified and underspecified. This solution also presents a significant reduction in costs for the development of NLI systems, while attaining greater visualisation inference abilities compared to traditional NLP approaches that use hand-crafted grammar rules and tailored models. This study also presents how LLM prompts can be constructed in a way that preserves data security and privacy while being generalisable to different datasets. This work compares the performance of GPT-3, Codex and ChatGPT across several case studies and contrasts the performances with prior studies.Item Beyond Predictive Learning Analytics Modelling and onto Explainable Artificial Intelligence with Prescriptive Analytics and ChatGPT(Springer Nature in conjunction with the International Artificial Intelligence in Education Society (IAIED), 2024-06) Susnjak TA significant body of recent research in the field of Learning Analytics has focused on leveraging machine learning approaches for predicting at-risk students in order to initiate timely interventions and thereby elevate retention and completion rates. The overarching feature of the majority of these research studies has been on the science of prediction only. The component of predictive analytics concerned with interpreting the internals of the models and explaining their predictions for individual cases to stakeholders has largely been neglected. Additionally, works that attempt to employ data-driven prescriptive analytics to automatically generate evidence-based remedial advice for at-risk learners are in their infancy. eXplainable AI is a field that has recently emerged providing cutting-edge tools which support transparent predictive analytics and techniques for generating tailored advice for at-risk students. This study proposes a novel framework that unifies both transparent machine learning as well as techniques for enabling prescriptive analytics, while integrating the latest advances in large language models for communicating the insights to learners. This work demonstrates a predictive modelling framework for identifying learners at risk of qualification non-completion based on a real-world dataset comprising ~7000 learners with their outcomes, covering 2018 - 2022. The study further demonstrates how predictive modelling can be augmented with prescriptive analytics on two case studies to generate human-readable prescriptive feedback for those who are at risk using ChatGPT.Item Relational assessment in a low-trust world(Taylor and Francis Group, 2024-05-31) Ramsey P; Cataloni SPrompted by concerns over student use of ChatGPT, staff teaching leadership and teamwork on a large university course experimented with an alternative way of assessing students’ learning. Past assessment practices emphasised individual reflection and quality assurance. Aiming for a more relational approach that is aligned with the course’s content, an oral review was employed. Staff conducted a review of learning with teams rather than individuals. Based on an in-depth staff review of the experience, this article explores the approach. The relational nature of the review was a dramatic departure from students’ previous experience of assessment, which some students found disconcerting. Staff identified key lessons that can be applied to future oral review assessments. Lessons learned involved how to balance the twin goals of quality assurance and personalised learning. Staff recognise the need to explain the approach to assessment, starting early in the course.Item The impact of ChatGPT on teaching and learning in higher education: Challenges, opportunities, and future scope(IGI Global, 2024-04-01) Li M; Khosrow-Pour MDBAThe integration of OpenAI's ChatGPT is reshaping higher education by transforming teaching and learning dynamics. This article delves into ChatGPT's impact, exploring opportunities, challenges, and future potential. ChatGPT's deployment in higher education offers interactive and adaptive classrooms, enabling personalized learning experiences. Educators use ChatGPT to enhance engagement, critical thinking, and tailor content, fostering innovative teaching. However, integrating ChatGPT also introduces challenges, including plagiarism detection concerns due to AI-generated assignments and potential impacts on writing skills and independent thinking. Addressing misinformation risks from AI content requires responsible usage guidelines. Looking forward, ChatGPT holds promise in higher education, as AI-enhanced collaborative classrooms redefine teaching. The symbiosis of ChatGPT with human instructors enhances effectiveness, providing real-time insights and boosting student engagement.