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Faculty Futures 2035: Human-Centred AI Education

Contents

Executive Summary

"Faculty Futures 2035" is a global initiative examining how to intentionally redesign faculty roles and institutional systems as AI becomes embedded in higher education. Rather than focusing on AI adoption as a technical problem, the project frames AI integration as a structural reconfiguration of academic authority, knowledge evaluation, and pedagogical responsibility—requiring coordinated change across individual, institutional, and system-wide levels. Findings from parallel workshops in Canada, India, and Australia reveal convergent structural pressures across different regulatory contexts, suggesting the need for a globally-informed but locally-adapted roadmap for sustainable faculty transformation by 2035.

Key Takeaways

  1. Faculty readiness requires institutional coordination, not just individual upskilling. Success depends on alignment across multiple university layers (individual competencies, collective professional norms, institutional structures) working together—not independently.

  2. Human-centred AI is a governance principle, not a rhetorical phrase. It operationalizes the commitment that humans retain accountability for academic judgment, even as technological systems mediate intellectual work.

  3. Convergence across countries signals structural, not superficial, change. The fact that faculty in Canada, India, and Australia identified nearly identical challenges despite different regulatory and institutional contexts suggests these are fundamental reconfigurations of academic work—not temporary adoption challenges.

  4. Assessment redesign is urgent and institution-specific. Traditional evaluation models are incompatible with AI-augmented learning. Faculty need coherent but locally-adapted frameworks that allow disciplinary professional judgment while maintaining clear standards.

  5. The next decade requires intentional deliberation before patterns embed by default. Decisions made now about faculty roles, authority, assessment, and institutional systems will shape higher education for the next 10+ years. Waiting for technology to "settle" risks letting structural decisions be dictated by technological momentum rather than educational values.

Key Topics Covered

  • Faculty readiness and professional capability in AI-augmented academic environments
  • Institutional alignment across individual, collective, and system-wide layers
  • Academic authority and epistemic judgment in contexts where AI mediates intellectual work
  • Human-centred AI as a design orientation and accountability framework
  • Assessment redesign and evaluation under AI-augmented learning systems
  • Professional development as structural system rather than episodic training
  • Ethical frameworks for responsible AI use in higher education
  • Workload and infrastructure barriers to faculty adaptation
  • Domain expertise and disciplinary knowledge as increasingly critical in the AI age
  • Pedagogical transformation toward human-AI collaboration models
  • Policy coordination across institutions and national contexts

Key Points & Insights

  1. Faculty readiness is not reducible to individual skills. AI literacy, prompt design, and tool fluency are necessary but insufficient. True readiness requires alignment across individual, collective (professional development), and institutional (structural systems) levels. When these move independently, institutional strain results.

  2. The central concern is not whether to use AI, but who maintains authority for academic judgment. As generative systems mediate intellectual work (drafting, synthesizing, analyzing, ideation), questions of epistemic authority and knowledge validation become critical. Human-centred AI maintains institutional agency by preserving clarity about accountability for academic standards.

  3. Convergence across three countries reveals structural (not coincidental) pressures. Despite operating under different regulatory regimes (Canada, India, Australia), faculty in all three contexts identified the same core issues: assessment redesign, workload constraints, fragmented institutional systems, lack of coherent guidance, and tension between rapid technological change and institutional structures designed before AI.

  4. Professional development frequently fails due to systemic constraints, not faculty resistance. Institutions often expect individuals to adapt to change while maintaining structures incompatible with continuous learning. Semi-digital infrastructure, fragmented knowledge systems, administrative overburden, and unclear policies undermine capacity-building efforts.

  5. Faculty see themselves evolving toward five new roles by 2035: (1) curators of knowledge, (2) critical validators of AI-generated insights, (3) designers of human-AI collaborations, (4) ethical stewards, and (5) cognitive elevation specialists—not simple content deliverers.

  6. Domain expertise becomes more, not less, critical in the AI age. AI cannot replace disciplinary knowledge, contextual judgment, or moral discrimination. Faculty emphasized that new literacies (data literacy, analytical thinking, basic programming awareness, mathematical reasoning) must complement, not substitute for, deep subject matter expertise.

  7. Assessment models are under structural pressure and lack institutional clarity. Faculty recognize that traditional assessment practices cannot remain unchanged in AI-integrated systems. They seek discipline-relevant, defensible guidance—not rigid rules—but institutions lack clear frameworks for calibrating expectations consistently across programs.

  8. Ethical complexities and "intellectual delegation by default" are emerging risks. Faculty observed unconscious outsourcing of thinking to machines among students and colleagues. The principle of "human in the loop" governance must be explicit, with faculty modeling ethical use, verifying outputs, attributing sources, and ensuring transparency.

  9. Strategic institutional actions must address five areas: (1) infrastructure enablement (access to AI tools, GPUs, labs), (2) mandated structured training, (3) workload rationalization, (4) pedagogical transformation, and (5) ethical and governance frameworks. None of these is primarily technical; all require policy and resource commitment.

  10. A global roadmap is emerging, but it cannot be universal policy. The initiative proposes an "unfinished design" template that is architecturally grounded in convergence (shared structural pressures) but must be refined through continued cross-context testing and local adaptation.

Notable Quotes or Statements

  • "Faculty futures 2035 emerged from the recognition that faculty readiness must be intentionally designed. It cannot be left to chance, nor can it rely solely on individual adaptation; rather it belongs to a global framing." — Dr. Akirim Shilbeeka (University Canada West)

  • "The central concern is not whether AI should be used. It is how institutions preserve clarity about who defines academic judgment in environments where technological systems increasingly mediate intellectual work." — Dr. Shilbeeka

  • "Institutional readiness is not reducible to individual competence. It concerns alignment across multiple layers of the university at the individual level, collective level, institutional institution level. When these levels move independently, strain appears—not always dramatically but steadily." — Dr. Shilbeeka

  • "AI is transformative but it is not replacement. Faculty will not simply deliver lectures but will become curators of knowledge, critical validators of AI-generated insights, designers of human-AI collaborations, and ethical stewards." — Dr. TP Singh (Chandigarh University, Uttar Pradesh)

  • "The uncomfortable truth: Faculty professional development often fails not because educators resist change but because system constraints them." — Transcript

  • "Wisdom, empathy, moral judgment and contextual understanding remain human capacities. The future of higher education will not be written by algorithm only. It will be co-authored by visionary institutions, empowered faculty, responsible governance and ethically guided intelligence." — Dr. TP Singh

  • "These are not three isolated national stories about a new tool. It is a shared reconfiguration of academic work... This reconfiguration of academic work is about how knowledge is produced as well as evaluated and governed." — Panel closing remarks

Speakers & Organizations Mentioned

Key Speakers:

  • Dr. Akirim Shilbeeka — Professor of Strategy, University Canada West; lead architect of Faculty Futures 2035
  • Dr. Sana Jamal — Center for Teaching Excellence, University Canada West; co-lead and methodology designer
  • Dr. TP Singh — Founding Vice Chancellor, Chandigarh University, Uttar Pradesh
  • Dr. Amit Mishra — Chandigarh University, Uttar Pradesh (recommendations synthesis)
  • Dr. Sandra Song — Founding Dean, School of Arts, Science & Technology, University Canada West; panel moderator
  • Dr. Ramandep Sei — Canterbury Institute of Management, Melbourne (mentioned as collaborative partner)

Institutions:

  • University Canada West (UCW) — Vancouver, BC; 254 faculty across 2 schools, hybrid delivery model
  • Chandigarh University, Uttar Pradesh (India) — NRF ranked #19 in India; multi-disciplinary (law, engineering, management, computing, pharmacy, healthcare); ~100 faculty participants; ~2,700 students
  • Canterbury Institute of Management (CIM) — Melbourne, Australia; privately-based, professionally-oriented curriculum, industry-aligned practice
  • University of Oxford — AI and Education research hub mentioned as initial professional connection point
  • India AI Impact Summit 2026 — Host venue for the panel discussion

Technical Concepts & Resources

Methodological Approaches:

  • Rapid Foresight Lab — Time-boxed, structured workshop format using rotational prompts to surface strategic insights quickly
  • Rotational Prompt Model — Faculty groups rotate through 6 tables with different prompts; fixed facilitators/notetakers capture emergent themes; 10-minute time boxes per prompt to compress discussion and focus on systemic rather than tool-level issues
  • Three-Layer Readiness Architecture — Individual faculty perspective → collective professional development → institutional responsibility; used to diagnose where tension and design interventions are needed
  • Structured Foresight Conversation — Deliberate design to enable strategic sense-making, not surveys or hypothesis testing

Conceptual Frameworks:

  • Human-centred AI (as governance orientation, not rhetorical phrase) — Preserves human accountability for academic standards while AI mediates aspects of knowledge production
  • Institutional agency — Capacity of universities to maintain control over core academic decisions despite rapid technological change
  • Epistemic authority — Who has legitimate power to validate knowledge claims, define understanding, set standards
  • "Intellectual delegation by default" — Unconscious outsourcing of thinking to machines (identified risk)
  • "Human in the loop" governance — Principle that human judgment, verification, and oversight remain essential

Competencies & Literacies Referenced:

  • AI literacy and fluency
  • Prompt design and tool experimentation
  • Data literacy
  • Basic programming awareness
  • Mathematical reasoning
  • Analytical thinking
  • Interdisciplinary curiosity
  • Ethical judgment in AI use
  • Domain expertise (emphasized as increasingly critical)

Infrastructure & Support Systems:

  • GPU access and dedicated AI labs
  • Coherent institutional policies on academic integrity, AI use, assessment
  • Workload planning aligned with continuous learning expectations
  • Professional development framed as structural system, not episodic workshops
  • Assessment redesign frameworks
  • Ethical and governance frameworks for AI integration

Policy & System Elements Discussed:

  • Quality assurance frameworks (e.g., BC's quality assurance framework mentioned for Canada)
  • Academic integrity policies
  • Curriculum design and change processes
  • Pedagogical autonomy vs. institutional guidance balance
  • Infrastructure and resource allocation
  • Workload and evaluation systems

Note on Data Quality: The transcript shows signs of technical transcription errors (repeated phrases, stutters, minor inaccuracies), but the core arguments, findings, and recommendations are clear and consistent across all three institutional contexts. Quotes have been cleaned of transcription artifacts while preserving original meaning.