If we are to teach and learn alongside AI (artificial intelligence) tools, we need to develop our collective AI literacy. There are some obvious (and some less obvious) implications for academic integrity and assessment that must be considered. But, where do we start?
Thanks to our colleagues at the MQ Writing Centre, here are some prompts for our own reflection as teachers, and for discussion with students.
Reflection questions for staff
1. What is the value of this assessment for students? How is it intended to support their cognitive development and disciplinary understanding, and what role does writing (reading, planning, drafting, editing, etc.) play in that development?
2. In your discipline, how important is it for students to be able to clearly and effectively articulate the type of thinking elicited in this assessment?
3. In professional fields related to your discipline, how important is it for experts to be able to communicate quickly and effectively with diverse audiences? (How) Does this assessment support students in practicing this type of communication?
4. Is the assessment product more important, equally important, or less important than process for student learning?
5. How do you view the “outsourcing” of written communication to AI tools? How would other professionals in your discipline view the use of AI tools for drafting their own written documents?
6. What (if anything) do you feel comfortable using AI tools for (in terms of producing written text)? What are the implications of this for this particular assessment? What are the implications in terms of student development across their course of study?
7. What (if anything) do you feel comfortable with students using AI tools for (in terms of producing written text)? What are the implications of this for this particular assessment? What are the implications in terms of student development across their course of study?
8. Based on your reflection, what would you like your students to get from a discussion on AI use in your unit/assessment? What conclusions would you like them to draw, and what kinds of questions will support them in developing these understandings? Are there any key points that you must emphasise or communicate to students?
Discussion questions for students
There are different ways to spur a discussion with students. The questions below can be adapted for an in-class or forum discussion.
Set 1: Academic integrity in AI use
1. What role does written communication play in this assessment/this unit/this discipline? How skilled are you with this type of communication, and what learning would you potentially miss if you did not practice consistent written communication during your course of study?
2. How comfortable would you feel with your lecturers using AI to
a. create an assessment task
b. create a rubric
c. mark and provide feedback on your written work
d. respond to one of your emails?
3. To what extent do you feel it is important to be graded on work that you have done? How much can you use AI in the writing process and still feel that you are submitting your work?
4. To what extent (if at all) should professionals in your discipline use AI tools to write documents and resources, and why? That is, how do you think experts should use things AI and why, and what do you think the boundaries for its use should be and why?
Set 2: Understanding the role and purpose of assessment in university
1. What do you think is the purpose of this assessment? In addition to assessing your knowledge/understanding, what skills and knowledge are you expected to develop or practice from the process of completing this task?
2. What is the purpose of reading and writing in terms of your cognitive and disciplinary development?
3. What is the purpose of a university degree? What are you here to learn, and why? How does the completion of assessment tasks support you in obtaining these skills and this knowledge?
4. What does success in this assessment look like to you?
Set 3: Critically interrogating the implications of AI use
1. What are the potential implications of conferring a professional degree upon someone who has relied upon AI to generate assessment tasks? How can/should universities offset related risks?
2. Although they can appear quite objective, AI programs are “trained” on the writing of (biased) humans. What are some of the implications of this training?
3. What voices and perspectives might be missing or exaggerated due to the way AI has been trained? How might this impact the kinds of answers AI might generate?
4. While AI programs have been trained on large amounts of writing, it is unlikely that they have been trained on information behind academic journal and database firewalls. What are the implications of this for the quality and reliability of information produced?
Resources to try
Share your experience
We welcome your thoughts in the comments below regarding writing and AI or talking to students about the use of generative AI tools. You can also contribute your ideas by emailing email@example.com.
Join the conversation: Thursday, 30th March 2023 from 13:00-14:00. Register free for a MQ Community Roundtable: Gen AI tools – implications for learning, teaching and assessment at Macquarie.
Acknowledgements: Post edited and reviewed by Olga Kozar, Daniel Anson & Karina Luzia.
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