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In the second of our annual reviews we turn our focus to the emerging role of AI across research and higher education. Across the research cycle from research methods to administration and teaching and learning, the debate around AI in higher education has shifted from theory to practice. Here we bring together ten insights from across the LSE Impact Blog’s coverage of AI, Data and Society in 2024. Enjoy!
Social science can no longer ignore the social actions of intelligent machines
Intelligent machines and AI interfaces are increasingly embedded in a range of social contexts. In turn these machines are themselves deeply shaped by the social and cultural milieu of their human creators. Milena Tsvetkova makes the case that social scientists should recognise and engage with the social properties of these new technologies.
To improve their courses, educators should respond to how students actually use AI
Drawing on the findings of the GENIAL project, which focused on how generative AI tools are used in practice by students in real time, Dorottya Sallai, Jon Cardoso-Silva and Marcos Barreto analyse how students use these tools differently across qualitative and quantitative subjects and offer recommendations for how educators can integrate these findings into their teaching and assessment plans.
Universities need more than off-the-shelf AI solutions
Following the release of ChatGPT Edu, OpenAI’s enterprise offer to universities, Daniela Duca assesses the landscape of AI adoption in higher education and the different and emerging AI options available to universities.
Can seeing like a spider change policy and the future of AI?
Research into the minds of other animals and particularly invertebrates raises questions about how we define and understand consciousness itself. Daria Zakharova discusses how creating an artistic interpretation of the mind of a spider can inspire new legislation and shed light on how we understand developments in new forms artificial of intelligence.
Writing assistant, workhorse, or accelerator? How academics are using GenAI
Reporting on a nationwide survey among researchers in Denmark, Serge P.J.M. Horbach, Evanthia Kalpazidou Schmidt, Rachel Fishberg, Mads P. Sørensen and colleagues find three clusters of attitudes towards GenAI use for research. They argue these variations reflect differences across disciplines and different knowledge production models.
In the age of AI, has long-form writing in higher education reached a dead end? Martin Compton and Claire Gordon discuss the unique aspects of the essay and introduce a manifesto to revitalise it.
If generative AI accelerates science, peer review needs to catch up
Studies have increasingly shown the widespread use of generative AI in research publications. Faced with the consequent uptick in the number of publications, Simone Ragavooloo argues that editors and reviewers should embrace AI tools to undertake the heavy lifting of statistical and methodological review and to allow them to focus on areas that require human expertise.
Should the generative AI scholar be fast or slow?
Across all disciplines norms are being established around the role of generative AI in research. Discussing a recent LSE Impact Blogpost on academic responses to AI, Eric A. Jensen suggests that clear, practical guidance needs to be developed by specific research associations and institutions.
Is it ethical to use generative AI if you can’t tell whether it is right or wrong?
The use of AI-generated images, text and code are becoming a normal occurrence in academic work. Mohammad Hosseini and Kristi Holmes reflect on a recent misadventure with AI image generation and suggest researchers ask themselves two questions to ensure they have sufficient expertise to protect the integrity of research, before using these tools.
AI can carry out qualitative research at unprecedented scale
The interactional skill of large language models enables them to carry out qualitative research interviews at speed and scale. Demonstrating the ability of these new techniques in a range of qualitative enquiries, Friedrich Geiecke and Xavier Jaravel, present a new open source platform to support this new form of qualitative research.
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Image Credit: LSE Impact Blog.
