AI AS CO-LEARNER
POSTHUMAN APPROACHES TO GRADUATE EDUCATION
Ryan Straight, Ph.D
College of Information Science
University of Arizona
OUR AGENDA
The Plan for Today
The Challenge
Posthuman Framework
Three AI Paradigms
Faculty Practice
Next Steps
WHO
Ryan Straight, Ph.D
Assistant Professor in Cyber and Information Operations
Director, MA{VR}X Lab
College of Information Science
University of Arizona
THE CHALLENGE
The Disruption
Graduate students as bounded learners
Comprehensive exams in an AI era
Committees evaluating “original” work
How do we reconceptualize “graduate learner” when AI
becomes an active participant in advanced
coursework, research, and scholarly development?
POSTHUMAN FRAMEWORK
Comparing Viewpoints
Traditional View
Individual scholars
+
AI tools
Posthuman View
Learning assemblages
of
human-AI entanglement
Theoretical Foundation:
- Braidotti: Nomadic subjectivity
- Barad: Agential realism
- Hayles: Distributed cognition
- Verbeek: Technology as mediator
THREE AI PARADIGMS
Three Frameworks for Understanding AI in Graduate Education
AI as Tool
“Sophisticated search engine”
- Passive assistance
- Neutral mediation
- Individual + tool
Challenge: Guide beyond instrumental use
AI as Co-Creator
“Intellectual partnership”
- Collaborative meaning-making
- Shared authorship
- Human-AI entanglement
Challenge: Mentor ethical co-creation
AI as Co-Learner
“Mutual adaptation”
- Non-human learning
- Assemblage evolution
- Distributed growth
Challenge: Design learning assemblages
GRADUATE EDUCATION CHALLENGES
Challenges and Responses
Emerging Challenges
Undisclosed AI use
Evaluating originality
Outdated assessments
Faculty Imperatives
Mentoring
Assessment
Standards
Preparation
How do we maintain academic rigor while embracing innovation?
FACULTY PRACTICE
From Individual Students to Learning Assemblages
Course Design Shift
Traditional: Individual students use AI tools
Posthuman: Design for human-AI assemblages
Implementation: Explicit collaborative assignments with process documentation
Assessment Evolution
Traditional: “Did they do it alone?”
Posthuman: “Did they engage thoughtfully?”
Implementation: Process-focused evaluation and portfolio documentation
Graduate-Specific Transformations
Assessment Updates
- Comprehensive Exams: Test assemblage capabilities, not recall
- Dissertation Defense: Evaluate process + content
- Oral Presentations: Probe understanding beyond AI-assisted writing
Research Methods Integration
- Traditional: Individual researcher methods
- Enhanced: Human-AI collaborative design
- Skills: Ethical AI integration, bias recognition, process documentation
Mentoring for AI-Integrated Futures
AI Literacy → AI Fluency → AI Wisdom
Initial Conversations
“How are you currently using AI?”
“Let’s establish guidelines together”
“How do you want to acknowledge AI collaboration?”
Assessment Framework
- Process over product: How did they think through collaboration?
- Transparency: Can they document AI interactions?
- Scholarly voice: Is their contribution clear?
Opportunities and Needs
Research Questions
- How do students experience AI co-learning?
- What new forms of scholarly agency emerge?
- How can committees assess collaborative research?
- What new research methods do we need?
Institutional Changes
- Program-specific guidelines (not blanket bans)
- Faculty development in posthuman approaches
- New assessment methods for collaborative work
- Ethics integration across curricula
PRACTICAL STRATEGIES
Implementation: Start Small, Think Big
Course Design
- Modify one assignment
- Require process documentation
- Include reflection components
- Maintain scholarly voice
Key Student Questions
- When does AI support vs. replace thinking?
- How do you maintain scholarly voice?
- How do you document AI contributions?
- What are ethical implications?
Mentoring Approach
- Co-develop guidelines
- Focus on when/how questions
- Prepare for AI-integrated careers
- Model thoughtful practice
Assessment and Development
AI Fluency Progression
- Literacy: Understanding capabilities/limitations
- Fluency: Ethical integration into practice
- Wisdom: “Networked wisdom” within assemblages
New Assessment Framework
- Focus on reasoning, not output
- Transparency + ethical reasoning
- Model thoughtful practice
KEY TAKEAWAYS
The Central Insight
AI isn’t just reshaping what we learn
It’s reshaping how we learn and who does the learning
Four Key Points
- Posthuman frameworks reveal limitations of bounded learner models
- AI paradigms progress from tool → co-creator → co-learner
- Faculty practice must evolve to support learning assemblages
- We must model thoughtful integration while mentoring the next generation
The Real Question
Not: Does AI belong in graduate education?
But: How do we engage thoughtfully with the changes already happening?
NEXT STEPS
Start Small, Think Big
Immediate Actions
- Have conversations
- Try one change
- Develop guidelines
- Model practice
Bigger Questions
- How might your discipline benefit?
- What challenges does your program face?
- How do we maintain rigor?
Implementation Resources & Tools
For Syllabi & Courses
- AI collaboration disclosure statements
- Process documentation requirements
- Academic integrity redefinitions
- Assessment criteria for AI-assisted work
For Departments & Committees
- Graduate student AI agreement templates
- Dissertation defense question banks
- AI collaboration evaluation rubrics
- Faculty conversation starter frameworks
What You Can Do Tomorrow
In Your Next Class
- Ask students about their current AI use
- Add AI disclosure to one assignment
- Include process documentation requirement
- Start “AI and Research” discussion
In Your Next Meeting
- Propose department AI discussion
- Ask advisees about their AI integration
- Share process documentation templates
- Begin collaborative guideline development
The Future is Collaborative
These are conversations we need to have together, as a community of graduate educators
QUESTIONS & DISCUSSION
Dr. Ryan Straight
ryanstraight@arizona.edu
ORCID: 0000-0002-6251-5662
ryanstraight.com | mavrxlab.org
MSU Denver GRADTALK | September 17, 2025
Further Reading
Adams, C., & Thompson, T. L. (2016). Researching a Posthuman World. Palgrave Macmillan UK. https://doi.org/10.1057/978-1-137-57162-5
Braidotti, R. (2013). The posthuman. Polity Press.
Ferrando, F., & Braidotti, R. (2019). Philosophical Posthumanism. Bloomsbury Publishing Plc. https://www.bloomsbury.com/us/philosophical-posthumanism-9781350059498/
Forss, A. (2015). Nursing’s Nightingale Needed a Lamp! In Technoscience and Postphenomenology: The Manhattan Papers (pp. 161–168). Lexington Books.
Hasse, C. (2020). Posthumanist Learning in Education. In Posthumanist Learning: What Robots and Cyborgs Teach us About Being Ultra-social (1st ed.). Routledge. https://doi.org/10.4324/9781315647661
Hayles, N. K. (1999). How we became posthuman: Virtual bodies in cybernetics, literature, and informatics. The University of Chicago Press. https://hdl.handle.net/2027/heb05711.0001.001
Hayles, N. K. (2006). Unfinished Work: From Cyborg to Cognisphere. Theory, Culture & Society, 23(7–8), 159–166. https://doi.org/10.1177/0263276406069229
Latour, Bruno. (2005). Reassembling the Social: An Introduction to Actor-Network-Theory. Oxford University Press, Incorporated. https://academic.oup.com/book/52349
Taddeo, M., & Floridi, L. (2018). How AI can be a force for good. Science, 361(6404), 751–752. https://doi.org/10.1126/science.aat5991
Taylor, C. A., & Hughes, C. (Eds.). (2016). Posthuman Research Practices in Education. Palgrave Macmillan UK. https://doi.org/10.1057/9781137453082
Tripathi, A. K. (2015). Postphenomenological investigations of technological experience. AI and Society, 30(2), 199–205. https://doi.org/10.1007/s00146-014-0575-2
Ulmer, J. (2017). Posthumanism as Research Methodology: Inquiry in the Anthropocene. International Journal of Qualitative Studies in Education, 30, 832–848. https://doi.org/10.1080/09518398.2017.1336806
Verbeek, P.-P. (2023). Postphenomenology and Ethics. In Technology Ethics. Routledge.
Wagen, W. van der. (2019). The Significance of ‘Things’ in Cybercrime: How to Apply Actor-network Theory in (Cyber)criminological Research and Why it Matters. Journal of Extreme Anthropology, 3(1), Article 1. https://doi.org/10.5617/jea.6895
Dr. Ryan Straight – ryanstraight@arizona.edu – MSU Denver GRADTALK