Transforming Learning: Exploring ‘TextGenEd’ and the New Era of Text Generation in Education
Djuddah Leijen
Institute of Foreign Languages and Cultures, Associate Professor of English Language
Helen Hint
Institute of Estonian and General Linguistics, Lecturer in Academic Literacy
Last year marked a milestone of the rapid advancement and accessibility of AI tools, leading many educators across the globe to consider and experiment how these technologies can be integrated into their teaching. As a result, a great number of seminars and webinars, such as the AI+Education Summit: AI in the Service of Teaching and Learning at Stanford University and Artificial Intelligence and Education organised by the Council of Europe and expert opinions and publications, such as, AI in Learning: Designing the Future, published by Springer and Artificial Intelligence Teaching Guide, published by Stanford University have been thrown at teachers and lecturers to help them navigate how they can use AI to enhance or compliment their teaching. In this post, we introduce one such resource, which we think offers great practical examples on how to implement and use AI tasks directly in your classroom.
In 2023, Annette Vee, Tim Laquintano, & Carly Schnitzler edited a peer-reviewed volume “TextGenEd: Teaching with Text Generation Technologies”, which features ready to use assignment to support student’s AI literacy. The best thing is, the collection is freely accessible through the WAC Clearinghouse.TextGenEd’s 50 assignments are published keeping in mind the pedagogy needed to support students’ i) AI literacy, ii) creative exploration, iii) ethical considerations, iv) professional writing, and v) rhetorical engagements using a variety of generative AI tools. This makes TextGenEd an essential resource for anyone who wants to integrate these technologies in their teaching.
If the aim in your course or teaching is to make sure students develop AI literacy skills, the first section of the book offers some practical tasks which you could include. For example, one assignment will challenge students to learn, understand, and access the influence of natural language processing (NLP) in chatbots such as ChatGPT using Python. Another assignment introduces students to reflect on chatbots as collaborators, readers, and writers through various tasks, which include various strategies to check any of the facts which a chatbot might produce. These assignments offer a great introduction to students (and instructors) about emerging AI literacy skills needed in higher education.
The second section offers students assignments to explore AI’s creative side. For example, specific tasks invite students to create poetry and various other types of compositions. What type of prompting is needed to push text generators to produce creative outputs, which also include images? As such, this section also explores additional generative AI tools such as DALLE-2 and Stable Diffusion to create children’s book images. Other tasks include exploring the role of language and purpose in both manual and LLM compositions combining meaning with expression and examining how LLM technology either mimics or modifies this expression. These tasks translate very well for classes which seek to respond to students’ curiosity to collaborate with an AI as a creative partner.
The third section of the book encourages students to evaluate the impact AI might have on their future careers. More specifically, the tasks challenge students to use AI responsibly and understand all the ethical implications that come with AI literacy. For example, some of the assignments have students reflect on some of the text generated output to determine whether certain cultural, social or gender bias might be included. Other assignments will challenge students to reflect on their own cultural and social identities by having a dialogue with a ChatGPT, for example, to test and understand their own arguments through text generated counter arguments. As part of creating critical thinkers, these tasks provide an excellent opportunity for both instructors and students to reflect on the deeper and more profound implications text generated models will have on our perceptions of our identity, beliefs, and knowledge.
Recognizing that not all academic writing is in essay form, the fourth section of TextGenEd offers teachers examples for using text-generating AI in various non-essay academic formats. Some examples from these assignments are working with technical reports, policy documents, and medical journals. Furthermore, the tasks are specifically targeted to consider how generative AI can support one’s profession as text editor, prompt engineer, or healthcare professional, but also how it can support one in the professional workplace by using AI tools to help summarise and translate into clear language workplace memos and policy documents. If you’re teaching courses which have a strong focus on preparing students for a profession, these tasks are a great way to introduce transferable skills.
In our opinion, the last section of the book might well contain essential tasks for instructors to introduce and for students to follow. As mentioned, the third section of this book challenges students to use AI to support critical thinking. The assignments in the final chapter challenges students to go beyond critical thinking and use Chatbots and AI tools to develop their rhetorical engagement with the content AI generates. For example, some of these assignments will help students to use these AI tools as chatting partners and peer reviewers. Other tasks challenge students to use prompt engineering to recreate specific genres which in turn are used as examples to help evaluate how well text generated output is able to enact specific styles. In other words, the final section invites students to become the engineer of the AI and become critical reviewers of how well an AI generated text meets our own personal and institutional ideas of valuable texts.
Given the pace of the field and instructor’s curiosity and creativity to integrate AI in their teaching, TextGenEd will continue to publish more peer reviewed assignments. Furthermore, if you’re feeling particularly creative with your own integrated examples, TextGenEd has a running CFP (https://wac.colostate.edu/repository/collections/continuing-experiments/) for continued experiments in teaching with text generation technologies. If your curious about possibilities and are looking for some concrete examples, TextGenEd is for you. If you have some ideas and are looking for some assignments to complement your teaching, TextGenEd is for you, and if you’re an early adopter and firm believer of generative technologies in your teaching, TexGenEd is for you! Finally, we encourage you to share you thoughts and experience, and if you are going to try one or some of these assignments, we’re interested to hear about your experiences, good and bad.
