AI and Education

Artificial Intelligences presents pedagogical challenges and opportunities for teachers and students alike. In a hands-on exploratory kind of way, together we will explore the significance of AI for the field of education and for your classroom.

It has become evident to that a primary concern of writing instructors associated with the MLA or NCTE or JCU (see links below) is that our students will offload their thinking to a Chat Bot. The iterative process model discussed by Graham (2023) was developed to insure that this would not happen.

In primary education, it is unlikely that our young students will be using LLMs. At the same time it is common to design lessons for these younger students that accidentally offload to other technologies, such as a calculator or a word processor, the actual thinking processes that we want our students to engage in or to master. This we want to avoid as well!

 Working individually, with a partner or in a small group, draft a proposal for a project due on 16 October that demonstrates your current understanding of AI (specifically LLMs) and education and considers the problem of offloading.

Resources | For additional information and background about ChatGPT and similar tools, please consult the following resources.

The AI Education Project | aiEDU is a non-profit that creates equitable learning experiences that build foundational AI literacy. Whether you have nine weeks or just five minutes, we have an engaging, free curriculum that’s easy to use.

CCCC-MLA Joint Task Force on Writing and Ai | The joint task force has been convened by the leadership of the Modern Language Association and Conference on College Composition and Communication, a chartered conference of the National Council of Teachers of English, to develop resources, guidelines, and professional standards around the use of AI and writing.

Consistent with the MLA and the NCTE Joint Task Force on Writing and Ai, learning experiences, whether technologically enhanced or not, are intentionally designed as processes for advancing student learning. Through their engagement in these intentionally designed learning experiences, the probability that students will experience real and sustained learning increases.

The first working paper of the MLA and NCTE Joint Task Force discusses risks and benefits of generative AI for teachers and students in writing and literature programs and makes principle-driven recommendations for the development of ethical policies and supports the development of critical AI literacy.

Risks to students | Students May…

  • Miss writing, reading, and thinking practice because they submit generative AI outputs as their own work or depend on generative AI summaries of texts rather than reading.
  • Not see writing or language study as valuable since machines can mimic these skills.
  • Experience an increased sense of alienation and mistrust if surveillance and detection approaches meant to ensure academic integrity are undertaken. Such approaches have been proven unreliable and biased; they can produce false positives that could lead to wrongful accusations, resulting in negative consequences for the students.
  • Face increased linguistic injustice because LLMs promote an uncritical normative reproduction of standardized English usage that aligns with dominant racial and economic power structures. Worldwide, LLMs may also perpetuate the dominance of English.
  • Have unequal access to the most elite tools since some students and institutions will be able to purchase more sophisticated versions of the technologies, which may replicate societal inequalities. The above risks could hurt marginalized groups disproportionately, limiting their ability to make autonomous choices about their expressive possibilities.
From: Graham. 2023. Post-Process but Not Post Writing.

Assigned readings

Howard, Mary. 2023. Artificial Intelligence to Streamline Your Teacher Life. Chapter 5: Prompting Strategies. Local. Global. Digital: Digcit Institute. pp. 17-19, 31-36, 45-54.

Graham, S. S. (2023). Post-Process but Not Post-Writing: Large Language Models and a Future for Composition Pedagogy. Composition Studies, 51(1), 162–168.