Collaborative Intelligence Strategies for Engineering Educators

Professors have a role in helping students develop conscious awareness of how AI affects their learning. I know this sounds obvious and feels like well-trodden ground, but bear with me because our research on the human experience of AI has something new to add. Our research identifies three psychological patterns that shape how students relate to AI: how easily AI responses blend into their thinking (Cognitive Permeability), how closely their identity becomes tied to AI performance (Identity Coupling), and their ability to shift the meaning of things in new contexts (Symbolic Plasticity). These patterns influence the roles students assign AI: Creator (sparks ideas they develop), Catalyst (exposes thinking gaps), Partner (true collaboration), Builder (strengthens frameworks), Framer (shapes understanding), Doer (handles grunt work), Co-Author (blended thinking), or Outsourcer (does their thinking entirely). When students automatically assign AI the Outsourcer role for developmental tasks, they miss the intellectual struggle that creates genuine capability. They may feel successful while their reasoning skills stagnate, creating false confidence that academic grades don't reveal. 1. Have Students Name AI's Role - Before AI-assisted assignments, require: "What role am I putting AI in?" Will it spark ideas you develop? Handle mechanical work? Do your thinking entirely? 2. Check Identity Investment - Help students notice when confidence ties to AI performance rather than genuine capability. Ask: "How would you feel sharing this if everyone knew AI's contribution?" "Would you feel confident without AI tomorrow?" 3. Track Cognitive Contribution - Students lose track of their intellectual input during AI collaboration. Ask: "What parts came from your reasoning?" "Could you recreate this logic next week?" "What did you contribute that AI couldn't?" 4. Designate Conscious AI-Free Zones - Reserve activities for independent work when building foundational skills. Frame as cognitive development, not prohibition. Examples: In-class problem solving, timed writing, verbal explanations requiring step-by-step thinking. 5. Prepare for Hybrid Intelligence - Frame AI integration as evolution in how intelligence works. Help students understand we're preparing for a future where humans and AI learn from each other. Have them consider: "How might your field evolve with hybrid intelligence?" "What human contributions become more valuable?" "How can you maintain agency while participating in collaborative thinking systems?" More here in our ideas on the AI course every university should teach, which is our response to the recent data and studies on graduate unemployment rates. https://lnkd.in/gVcjiey8

Several weeks into Spring 2025, my teacher partner Terry Underwood and I ahve made a breakthrough in my writing classroom: when students design their own assessment outcomes for AI integration, they develop approaches that far exceed our expectations. This insight didn't come from implementing rigid AI policies, but from trusting students to critically investigate AI's role in their writing process. Since January, I've watched this play out in remarkable ways. Rather than prescribing how AI should be used, we established a simple framework: first drafts remain primarily AI-free, creating space for students to develop their authentic voice. But then something fascinating emerged – students began designing sophisticated criteria for how AI might serve as a research companion and revision partner. What struck me most was this: Given the freedom to define success metrics, students naturally gravitated toward nuanced, responsible approaches to AI integration. They're not just asking "Can AI help me write?" but "How can I thoughtfully evaluate and integrate AI tools to enhance my writing process?" Some might say this level of student agency is risky. But consider what we're fostering: 1. Critical evaluation of AI tools - Students develop their own criteria for when and how AI can enhance their writing 2. Authentic writing ownership - By starting with AI-free drafts, students maintain their voice while exploring AI as a collaborative tool 3. Thoughtful technology integration - Integration decisions emerge from student experimentation rather than top-down mandates 4. Research-based experimentation - Students document and analyze their AI interactions, building an evidence base for effective use 5. Student-driven assessment - Success metrics reflect genuine student priorities and writing goals We're not just teaching writing with AI – we're empowering students to define what successful AI integration looks like. The focus isn't on controlling use, but on developing wisdom through guided experimentation and reflection. The real breakthrough comes from this fundamental shift: placing students at the heart of designing frameworks for AI in writing practice. When we trust their capacity for critical thinking and responsible innovation, they show us new possibilities for meaningful AI integration. #WritingPedagogy #StudentAgency #AIWriting #Spring2025 #WritingClassroom Rob Nelson Pat Yongpradit Scott Sommers, PhD Phillip Alcock Thom Markham, Ph.D. Jessica L. Parker, Ed.D. Jessica Maddry, M.EdLT Kimberly Pace Becker, Ph.D. Mike Kentz Rob Nelson Jason Gulya

How Mechanical and Materials Engineers Can Start Using AI in Their Work Artificial Intelligence is no longer limited to computer science, it’s becoming an essential tool across disciplines, including engineering and academic research. For mechanical engineers, materials scientists, and educators, here are some practical ways to begin integrating AI into your workflow: 1. Automated Literature Reviews Tools like Elicit, Connected Papers, and ResearchRabbit use AI to identify relevant studies, suggest related work, and even generate summaries; saving hours of manual searching. 2. Data Analysis and Visualization AI-integrated platforms (e.g., PandasAI, ChatGPT Code Interpreter) can help analyze experimental data such as stress-strain curves, thermal profiles, or SEM image results. This can be particularly useful for high-throughput testing or large datasets. 3. Assistance with Simulations For those working with FEA or thermodynamic modeling (e.g., using COMSOL, ANSYS, or CALPHAD), AI tools can help debug code, suggest boundary conditions, or optimize parameters more efficiently. 4. AI in Teaching and Assessment Educators can use AI to generate quizzes, explain complex topics in simpler terms, and even provide feedback on written assignments. It can also support personalized learning pathways for students. 5. AI for Research Planning GPT-based tools can assist with writing research proposals, identifying potential research gaps, and even outlining experimental plans. 6. Exploring AI-Driven Design Algorithms like genetic algorithms, reinforcement learning, or neural networks can be trained to assist in materials discovery, structural optimization, or predictive modeling. Getting Started: • Choose one task from your current workflow (e.g., paper summary, data cleaning, teaching content creation). • Use a trusted AI tool to assist and not replace the process. • Evaluate and refine your use of the tool based on outcomes. AI is not a replacement for engineering knowledge; it’s a powerful extension of it. If you’re already using AI in your work, what tools have been most helpful to you? #AIinEngineering #MechanicalEngineering #MaterialsScience #AcademicResearch #EdTech #CALPHAD #FEA #PhDLife

Quotations 📚 “AI is reshaping student engagement—but institutional efforts remain fragmented.” 📚 “Beyond introducing a new student–AI collaboration, AI is reshaping traditional relationships between students, faculty, peers, and content.” 📚 “The real opportunity lies in using AI as an intelligence layer that reveals patterns of learning and engagement once invisible to faculty.” 📚 “AI can broaden the scope of peer engagement by exposing students to more diverse perspectives.” 📚 “Responsible engagement with AI requires more than technical training—students must develop critical thinking, ethics, and human-centric skills.” Key Points 📚 The report draws on 106 global case studies, identifying 24 methodologies for AI-enhanced engagement across six dimensions: 1. Faculty interaction 2. Peer exchange 3. Content & assessment 4. Instructional delivery 5. Experiential & applied learning 6. Environment & inclusivity 📚 AI introduces new relational dynamics: students often turn first to AI for explanations, sometimes bypassing peers or faculty. 📚 Four opportunities stand out: – Deeper faculty–student engagement (mentorship, insights from learning data). – Broader peer-to-peer exchange (AI-mediated debates, diverse perspectives). – Richer student–content interaction (interactive readings, AI-integrated assessment). – Responsible human–AI collaboration (critical thinking, ethics, creativity). 📚 Emerging tools include: AI tutors, predictive analytics, faculty avatars, adaptive micro-learning, AI-mediated peer discussions, XR tutors, inclusive content creation, and AI learning assistants. 📚 Positive impacts include higher exam scores, greater engagement, and inclusivity gains—but risks of over-reliance, superficial learning, and weakened human connections persist. Headlines 📚 “AI Rewires the Student Experience: From Faculty Feedback to Peer Debate” 📚 “106 Case Studies, 24 Methods: A Global Map of AI-Enhanced Engagement” 📚 “Beyond Efficiency: AI as a Relational Actor in Higher Education” 📚 “Responsible Human–AI Collaboration Is the New Literacy” Action Items (Strategic Moves for CEOs & Education Leaders) 📚 Invest in institution-specific AI tutors and feedback loops—build oversight into AI–student interactions. 📚 Use AI to enhance—not replace—faculty roles, freeing time for mentorship and high-level dialogue. 📚 Expand peer engagement with AI-mediated tools that foster debate, diverse perspectives, and civil discourse. 📚 Align AI adoption with inclusivity goals—apply EDI checks in course design and content generation. 📚 Strengthen AI literacy frameworks for students: understanding systems, critical thinking, ethics, creativity. 📚 Create governance policies for AI use in classrooms—guard against over-reliance and ensure meaningful engagement. See also - https://lnkd.in/gbcF3f34 #AIinEducation #FutureOfLearning #StudentEngagement #EdTech #ExecutiveStrategy #ResponsibleAI #AILeadership #DigitalTransformation