Five Guiding Principles for Navigating Artificial Intelligence in Students as Partners Practice to Preserve Pedagogical Trust

Synthesis: Matthews (2025), in an editorial for the International Journal for Students as Partners, argues that Trust is fundamental to pedagogical relationships and therefore to students-as-partners practice in the age of AI. She warns that AI advances have amplified deficit views of students as cheaters or victims, a framing that risks fracturing learner–teacher relationships — especially when 83% of students in a large multi-institutional study (8,000+ respondents) reported using AI. Against the rise of "AI shame" and surveillance, she advocates pedagogical trust — a confident, reciprocal learning relationship open to uncertainty and co-navigated through dialogic decision-making and shared sense-making. To guide practice, she offers five principles for navigating GenAI together with students: (1) cultivate open and curious conversations; (2) acknowledge the emotional and vulnerable lived experiences of AI use; (3) make the role of AI visible and negotiable in learning activities; (4) create consistent opportunities for shared learning and reflection; and (5) ground ethical norms in shared values and a collective moral compass. Each principle pairs with questions for educators and students to explore together, positioning partnership as a counter to AI shame and surveillance.

Pedagogical Trust in the Age of AI

Trust has always underpinned pedagogical relationships, enabling or limiting the possibilities for partnership, participation, and learning. Matthews draws on the conception of trust as "a confident relationship with the unknown" (Botsman, 2017) to argue that higher education now confronts the deep unknown of Generative AI. In response, she advocates pedagogical trust — a confident, reciprocal learning relationship between students and teachers that is open to uncertainty and co-navigated through dialogic decision-making. This framing casts learning as a fundamentally social, relational process of meaning-making, growth, and connection, echoing arguments that AI advances should reaffirm — rather than displace — the centrality of human relationships in Higher Education.

Students-as-partners practice emerged precisely as a challenge to the entrenched assumption that students cannot be trusted to collaborate alongside staff in shaping teaching, learning, and assessment (Cook-Sather et al., 2021). Matthews warns that advances in AI have amplified deficit views of students as either cheaters or victims of inadequate education, threatening to fracture learner–teacher relationships and limit collective capacity to respond to AI. The worry is amplified by a large multi-institutional study in which 83% of over 8,000 students reported using AI (Chung et al., 2025), and by evidence that roughly half used AI for Feedback even while recognizing it may be less reliable than human teacher feedback (Henderson et al., 2025).

Partnership as Counter to AI Shame and Surveillance

Matthews observes a worrying trend, exemplified at her own university, of "AI shame" shutting down conversations about AI use — many people feel afraid to admit they use AI or to discuss its environmental and social harms, fearing judgment or trouble. When conversations close off, the trust sustaining learning partnerships risks being fractured. She positions student–staff partnership as the counter-narrative: an open, non-judgmental invitation that honors the relational foundations of education. Emerging partnership-based practices include students and staff co-creating how-to guides on AI (University of Sydney), co-designing an AI policy in first-year writing courses (Georgetown University), and co-creating and co-reflecting on AI in assessment within medical programs (University College London). These collaborative explorations foster — instead of fracture — pedagogical relationships through shared sense-making.

Matthews draws on Bearman and Ajjawi's (2023) metaphor of "black box pedagogy," framing AI relationally as more than a mere tool, with meaning arising through the interactions of people and what they produce with AI. This relational view requires preparing students to learn amid uncertainty in an AI-mediated world, demanding heightened ethical and critical attention. Student–staff partnership is especially well-suited to supporting navigation of such complexity and uncertainty, keeping the relational core of Higher Education intact even as knowledge itself is enacted as dynamic, provisional, and co-constructed.

Five Guiding Principles for Navigating AI Together

Matthews expands on her earlier five propositions for genuine partnership practice (Matthews, 2017) to offer five principles that honor and imbue pedagogical trust:

1. Cultivate open and curious conversations together. Trust flourishes in spaces honoring curiosity and vulnerability, where AI is treated not as a problem to be solved but a mystery to explore collectively — rooted in care, deep listening, and the willingness to sit with uncertainty.
2. Acknowledge and work with the emotional and vulnerable lived experiences of AI use. AI awakens a spectrum of feelings — hope, fear, frustration, wonder — felt by students and educators alike; recognizing this shared vulnerability is an act of care that invites empathy and makes it safe to seek support.
3. Make the role of AI visible and negotiable in learning activities. Transparency is more than disclosure; it is a collective act of witnessing and negotiating the shifting boundaries of Learner Agency, authorship, and Creativity, making the invisible visible and inviting ongoing dialogue about AI's place in co-created learning.
4. Create consistent opportunities for shared learning and reflection on AI. Learning with AI is a journey, not a moment; partnership commits to sustained, collective spaces where students and educators reflect, question, and imagine anew — including ensuring those whose knowledge has traditionally been devalued are included.
5. Ground ethical norms in shared values and a collective moral compass. Ethics is not a checklist but a living commitment to justice, care, and respect that emerges from shared values, demanding ongoing reflection, humility, and a fierce commitment to equity and inclusion.

Each principle is accompanied by questions for students and educators to explore together, translating partnership into a living practice rooted in care, vulnerability, and communal wisdom. Together they refuse easy answers, offering instead a pathway where pedagogical trust is nurtured through relationship, dialogue, and shared commitment.

What this means for practice

• Instructors. Open classroom conversations about AI as a shared mystery to explore rather than a problem to solve, since "AI shame" closes down dialogue and fractures the trust that sustains learning partnerships.
• Instructors. Make the role of AI visible and negotiable in learning activities: treat transparency as ongoing collective negotiation of agency, authorship, and creativity rather than a one-off disclosure statement.
• Instructors. Work with the questions published alongside each of the five principles — for example, "How do we hold space for the full range of emotions AI stirs within our learning community?" — rather than presenting the principles as rules to comply with.
• Faculty developers. Build consistent, sustained spaces for shared reflection on AI instead of one-off workshops, since learning with AI is a journey rather than a moment and partnership requires recurring collective conversation.
• Institutions. Choose trust over surveillance: deficit framings of students as cheaters or victims damage learner–teacher relationships, while partnership grounded in respect, reciprocity, and shared responsibility sustains public trust in higher education.

Limitations

• The piece is an eight-page editorial by a single author, not an empirical study: it collects no data, reports no sample, and offers the five principles as proposals rather than tested interventions.
• Its reading of the current climate rests on the author's informal observations at one institution, the University of Queensland (55,000 students enrolled), where she noticed students and staff moving to "AI shame."
• The supporting prevalence figures are borrowed from other studies — 83% of over 8,000 students across multiple institutions reported using AI (Chung et al., 2025), and about half used it for Feedback (Henderson et al., 2025) — so the argument depends on secondary evidence it does not itself evaluate.
• The partnership examples cited (a University of Sydney student-staff AI guide, a Georgetown first-year writing AI policy, University College London medical assessment co-creation) come from others' published accounts rather than from evidence examined here, and the author explicitly invites further critical discussion and empirical research.

Citation

Matthews, K. E. (2025). Five guiding principles for navigating artificial intelligence in students as partners practice to preserve pedagogical trust. International Journal for Students as Partners, 9(2), 1–8.