Call for chapters for an edited volume titled Generative AI in Higher Education: The Good, the Bad, and the Ugly, for which we have a contract with Edward Elgar Publishing in the UK.
Although a few books and an anthology on generative AI in higher education exist, ours will represent a multitude of perspectives to show that the topic is far more complex and thought-provoking. Thus, we intend to provide the reader with a book in a format that would offer a relativist rather than a positivist standpoint on the use of generative AI in higher education. The proposal was reviewed by three independent reviewers and received overwhelmingly good feedback. A very tentative table of contents is below. If you would like to contribute to any of those alone or with your colleague(s), please let us know and contact us firstname.lastname@example.org and/or email@example.com.
Alternatively, please feel free to suggest a topic/focus of your own, as long as it falls under either “the good”, “the bad”, or “the ugly” and remains within the context of higher education (teaching, learning, research, end the like). The chapter may be empirical or conceptual, but it should be argumentative. The maximum length of the chapter should be no more than 5000 words, all-inclusive.
The timeline for the rest of the process is quite tight:
Abstract of no more than 300 words in which you specify your argument September 25, 2023
Receipt of full chapters February 2024
Review and editing of full chapters February-April 2024
Submission of whole book manuscript June 2024
Kätlin and Riina
Tentative table of contents
1. Introduction to the volume
A general introduction to the volume. Setting the scene for the contributions in book, explaining its purpose and structure. Brief introduction of chapters in the book.
2. Contextualizing the contemporary generative AI with the past voices of Heidegger, Marx, Dreyfus (and others).
A chapter on pairing the emergence and exponential use of generative AI with several philosophical viewpoints from the past introduces a phenomenological take.
3. Why we should welcome generative AI: General benefits in (online) education.
A chapter on how to responsibly use generative AI since the technology is likely to stay.
4. Generative AI as an enabler for educators: Practical tips for generative AI usage in teaching.
A chapter on how generative educators can use AI to reduce their workload on pairing the generative AI with activities that still require the teaching and mastery of other, non-generative AI skills.
5. Generative AI as an enabler for students: Practical tips for generative AI usage in learning.
A chapter on what students need to know about AI and how they can benefit from using AI in the learning process.
6. Generative AI as a tool for optimization and efficiency in academic research
A chapter on the merits of generative AI use in academic research – why walk if there’s a car?
7. Generative AI as a tool for optimization and efficiency in education.
Optimization and efficiency have always been some of the cornerstones of the underlying capitalist world order. In a context where education is underfunded, generative AI might help cut costs and pave the way for the survival of higher education institutions.
8. Generative AI as a prerequisite for competitiveness in the labor market.
A chapter on the importance of generative AI skills while entering today’s labor market.
9. Generative AI as a disabler of imagination, creative thinking, experimentation, and self-expression.
A chapter on the potential loss of general creative abilities.
10. Generative AI as a disabler of cognitive development.
A chapter on the potential loss and/or underdevelopment of general cognitive skills.
11. Generative AI as a circumscribing tool to broaden one’s interpretive/mental horizon.
A chapter on how generative AI circumscribes our ability to experience and understand complex social reality.
12.Generative AI as a closed loop: the Nokia snake phenomenon.
A chapter on how the single-minded use of Generative AI will eventually lead to reduced diversity of ideas and a situation where the AI’s output is its own input.
13. Generative AI as a disabler of equality.
A chapter on how the use of generative AI takes from, rather than adds to equality.
14. Generative AI as a research disabler: why should we still walk even if we have a car?
A chapter argues that AI inhibits the emergence of high-quality academic research.
15. Generative AI and issues of copyright.
A chapter on the currently ambiguous understanding of copyright and authorship issues while using the tool (e.g., potential plagiarism, etc.).
16. Generative AI and an individual moral dilemma in teaching and/or research.
Descartes says “I think, therefore I am”; if I no longer think but only generate prompts, does it t mean that I no longer am?
17. The social moral dilemma of generative AI use in teaching and/or research.
A chapter on the use of generative AI in generating research output/applying for research grants/evaluating students, etc.