A Transparency Index Framework for Machine Learning powered AI in Education

The increase in the use of AI systems in our daily lives, brings calls for more ethical AI development from different sectors including, finance, the judiciary and to an increasing extent education. A number of AI ethics checklists and frameworks have been proposed focusing on different dimensions of ethical AI, such as fairness, explainability and safety. However, the abstract nature of these existing ethical AI guidelines often makes them difficult to operationalise in real-world contexts. The inadequacy of the existing situation with respect to ethical guidance is further complicated by the paucity of work to develop transparent machine learning powered AI systems for real-world. This is particularly true for AI applied in education and training. In this thesis, a Transparency Index Framework is presented as a tool to forefront the importance of transparency and aid the contextualisation of ethical guidance for the education and training sector. The transparency index framework presented here has been developed in three iterative phases. In phase one, an extensive literature review of the real-world AI development pipelines was conducted. In phase two, an AI-powered tool for use in an educational and training setting was developed. The initial version of the Transparency Index Framework was prepared after phase two. And in phase three, a revised version of the Transparency Index Framework was co- designed that integrates learning from phases one and two. The co-design process engaged a range of different AI in education stakeholders, including educators, ed-tech experts and AI practitioners. The Transparency Index Framework presented in this thesis maps the requirements of transparency for different categories of AI in education stakeholders, and shows how transparency considerations can be ingrained throughout the AI development process, from initial data collection to deployment in the world, including continuing iterative improvements. Transparency is shown to enable the implementation of other ethical AI dimensions, such as interpretability, accountability and safety. The 3 optimisation of transparency from the perspective of end-users and ed-tech companies who are developing AI systems is discussed and the importance of conceptualising transparency in developing AI powered ed-tech products is highlighted. In particular, the potential for transparency to bridge the gap between the machine learning and learning science communities is noted. For example, through the use of datasheets, model cards and factsheets adapted and contextualised for education through a range of stakeholder perspectives, including educators, ed-tech experts and AI practitioners

Technology transfer practices and strategies: Issues for Nigerian construction organisations and for research

Technology transfer is increasingly being seen as an important issue for economic development and growth. Arguably, this is why developed and especially developing countries are very keen on technology transfer. However, there are those that contend that organisations in the Nigerian construction industry are not as engaged in technology transfer as many would like, and that potential benefits of doing so are not realized. In the same vein, the challenges that confront construction organisations in Nigeria in terms of technology transfer have received very little empirical studies. This paper, therefore, presents a thorough review of literature on strategic issues and choices that Nigerian construction organisations face in grappling with technology transfer, together with associated challenges. Consideration is also given to what technology transfer actually means to such organisations, and how this is viewed in line with other terms such as innovation. The paper argues and concludes that the challenges that confront construction organisations in Nigeria are multifaceted and likely to impact on their strategic choices. In the same vein, it is also argued that these have implications for researchers attempting to investigate technology transfer practices and strategies in construction organisations in Nigeria in terms of their choice of research strategy and design