Legitimacy Under Algorithmic Authority
A relational diagnostic of leadership education in AI-mediated contexts, introducing the SCIS framework for examining legitimacy across surface, cultural, institutional, and systemic domains.
Matthew Agustin
Responsible Futures Designer & Innovation Architect
Research, strategy, and relational design for AI-enabled systems that protect human agency and public good.
Currently Director of Innovation at Responsible Innovation Lab, where I develop approaches for governing, designing, and implementing AI in ways grounded in context, accountability, and long-term public value.
Responsible Innovation Lab
AI governance & implementation
Research on AI, learning & agency
Frameworks, fieldwork & reflective tools
Recent & Upcoming
Current signals from the work.
Selected recent publications, learning experiences, and timely developments from across the portfolio.
Human Judgment in the Age of AI
A self-paced, case-based course on how AI participates in judgment by shaping interpretation, possibilities, recommendations, responsibility, authority, and review.
Research architecture
A body of work on how AI changes human participation.
My work examines how AI systems shape the conditions under which people interpret, learn, decide, create, trust, and remain accountable. The projects, frameworks, and publications on this site move through four connected pathways.
Relational AI
How AI systems become consequential participants in human judgment, meaning-making, trust, accountability, and institutional life.
Learning, judgment, and agency
How AI-mediated learning systems can support reflection, co-regulation, epistemic agency, and learner participation without substituting for human growth.
Governance, legitimacy, and responsibility
How organizations and public-interest systems can keep authority, accountability, contestability, and responsibility visible as AI becomes embedded in decision-making.
AI-era readiness and public value
How people, institutions, and communities can build the literacy, foresight, and participation capacities needed to navigate AI-mediated life with dignity and purpose.
Selected work
Frameworks, methods, and strategies for accountable AI futures.
Research-grounded work for protecting agency, strengthening accountability, and shaping AI in context — where it is used and who it affects.
Available for research collaboration, responsible AI strategy, framework development, learning design, workshops, and public-interest AI projects.
Flourishing AI Integrity Framework
FAIF is a values-first framework for aligning AI design, adoption, and governance with trust, dignity, and responsible innovation. It helps organizations examine cognitive integrity, ethical grounding, relational agency, technical fluency, and long-term stewardship before AI systems become normalized.
Framework
Responsible AI
Relational AI Depth Framework
RADF is a 7-level framework for understanding how human–AI engagement deepens from ambient automation to co-creative partnership. It helps users, educators, designers, and institutions ask what kind of relationship an AI interaction is creating, what forms of influence emerge, and what responsibilities are required to preserve agency, authorship, and ethical integrity.
Relational AI
Agency
Relational Integrity in AI Framework
RIAF defines relational integrity as the discipline of keeping AI-mediated support truthful, bounded, and non-substitutive. It helps teams evaluate whether an AI system preserves human agency, meaning-making, emotional boundaries, relational continuity, and visible accountability as it enters learning, care-adjacent, civic, or institutional contexts.
Relational Integrity
Agency
TextWalk
TextWalk is a steady co-reader that helps people move through documents by mapping structure, clarifying ideas, and inviting deeper modes of understanding. It protects learning integrity by supporting direct engagement with the text rather than summarizing around the reader, answering for them, or replacing their judgment.
Co-Reader
Learning Integrity
Ro
Ro is a Socratic AI coach that strengthens reflection through questions rather than answers. It supports learning, civic reasoning, and ethical decision-making while staying transparent about its limits and preserving user agency, dignity, and judgment.
Socratic Reflection
Civic Reasoning
Vireo
Vireo is a reflective thought partner for shaping short public messages that carry a truth, provocation, or vision for change. It helps people clarify voice, explore multiple creative directions, connect their message to values, and keep authorship transparent throughout the human–AI collaboration.
Co-Creation
Values Reflection
Human-Centered AI Literacy Course
The Human-Centered AI Literacy Course treats AI literacy as an interpretive and relational capacity, not a checklist of tools or technical skills. It helps learners notice how judgment, responsibility, trust, and norms shift in AI-mediated contexts so discernment can develop before adoption, policy, or performance pressures take over.
AI Literacy
Discernment
AI for Principled Innovators
AI for Principled Innovators helps learners move from mindset to MVP by using AI as a co-pilot while keeping human judgment, accountability, and community trust at the center. Across five modules, it builds habits of foresight, disciplined scoping, blind-spot detection, and carry-forward stewardship so innovation can move quickly without losing integrity.
Responsible Innovation
MVP Practice
AI Wisdom Education
AI Wisdom Education reframes AI learning from tool fluency toward discernment, ethical imagination, and relational agency. It gives educators classroom lenses and routines—such as Pause-and-Plan, Voice-Before-AI, Trace-Your-Reasoning, and Uncertainty Marking—to help learners collaborate with AI without over-relying on it or losing their own voice.
AI Wisdom
Discernment
The Work of You
The Work of You is a guided reflection framework for exploring identity, values, becoming, and contribution without turning self-understanding into a test, therapy, or template. It offers a spacious sequence of prompts that helps people name what matters, honor their own pace, and shape a more agency-rich story of the work and future they are moving toward.
Reflection
Identity
Course Architect Pro
Course Architect Pro helps educators, facilitators, nonprofits, and learning teams turn early ideas into clear, inclusive, evidence-based learning experiences. It emphasizes structure over content dumping, using learning science, accessibility cues, ethical design lenses, and human judgment to create adaptable courses, modules, workshops, and trainings.
Learning Design
Responsible AI
Youth Futures Suite
Youth Futures Suite is a modular learning pathway for helping young people build agency, ethical leadership, civic imagination, and readiness for a changing world. Anchored by a flagship scenario-based leadership course and supported by open-access micro-modules, it is designed to be globally adaptable, youth-centered, and aligned with responsible innovation, inclusion, and public-good futures.
Youth Futures
Ethical Leadership
AI Literacy Media Lab / Echo ’26
AI Literacy Media Lab / Echo ’26 is a 7½-week participatory learning cycle where students, educators, and civic learners become public educators of AI. Participants create short videos, podcasts, or interactive resources grounded in the Six Pillars of AI Literacy, turning learning into open-access public knowledge that strengthens trust, equity, and civic participation.
AI Literacy
Public Media
Decision Loom
Decision Loom is a values-in-the-loop simulator for practicing responsible decision-making under pressure. It turns dilemmas such as speed versus safety, efficiency versus equity, and innovation versus integrity into interactive scenarios that help leaders, educators, nonprofits, and civic teams see tradeoffs clearly before choices become commitments.
Decision Support
Values-in-the-Loop
Co-Creation Stack
Co-Creation Stack is a two-tier architecture for helping institutions adopt AI responsibly without compromising dignity, trust, or local context. Its inner layer builds relational readiness through shared values and reflective practice, while its outer layer translates those commitments into governance modules, use-case filters, oversight rhythms, and practical guardrails.
AI Adoption
Relational Readiness
Little Responsible Innovators
Little Responsible Innovators is a children’s picture book series for ages 5–8 that introduces responsibility, fairness, inclusion, curiosity, and stewardship through emotionally resonant stories. With classroom activity guides and pilots in schools, libraries, and family literacy programs, it offers an early pathway for helping children ask not only what they can build, but why it matters.
Children’s Books
Ethical Imagination
AI Governance Simulation Lab
The AI Governance Simulation Lab is a turn-based learning environment where participants role-play governance decisions involving emerging technologies under uncertainty. It surfaces stakeholder tensions, value drift, trust dynamics, and systemic consequences so participants can practice judgment, accountability, and civic imagination without being pushed toward a single "correct" answer.
Governance
Simulation
SCIS Framework
The SCIS Framework is a diagnostic lens for examining how legitimacy is formed, strained, or displaced when algorithmic systems participate in leadership, education, and governance. It maps legitimacy across Surface, Cultural, Institutional, and Systemic domains, helping teams see where authority appears credible, becomes culturally recognizable, gains procedural validation, or diffuses responsibility across sociotechnical systems.
Legitimacy
Hybrid Authority
AI Transparency Challenge
The AI Transparency Challenge is an interactive prototype that asks users to examine an AI scenario through distinct ethical lenses, including fairness, autonomy, care, accountability, and justice. It turns transparency into a structured reasoning practice by surfacing benefits, risks, named tensions, guardrails, recommendations, and trust checks rather than treating responsible AI as a single generic answer.
Transparency
Ethical Lenses
Applied Prototype Lab
From research concepts to working tools.
I turn selected responsible AI, learning, and judgment frameworks into interactive tools for practice and reflection. Current builds use Lovable for rapid interface development, GitHub for versioned iteration, and manual code refinement to keep the experience usable, accessible, and faithful to the underlying concept. Rather than treating prototypes as productivity tools alone, this work explores how interactive environments can support agency, repair, interpretation, and responsible participation.
Interpretation Detective
A learning environment for practicing interpretation, perspective-taking, and reflective judgment. The tool guides users from observation and investigation toward interpretation, reconsideration, and reflection.
Interpretation
Reflective judgment
Learning design
AI Repair Commons
A repair-analysis environment for examining problematic AI responses and shaping better follow-up. The tool focuses on what went wrong, what can be repaired, what transfers to future use, and where boundaries or uncertainty should remain visible.
AI repair
Responsible use
Transfer
Build workflow
Lovable for interface prototyping · GitHub for versioned iteration · manual refinement for accessibility, usability, and conceptual fidelity.
Research & Frameworks
Selected research artifacts for understanding AI, agency, and public value.
A curated entry point into publications, preprints, frameworks, and research directions exploring how AI shapes learning, judgment, participation, governance, and institutional life. For the full record, visit Writing & Publications.
Academic service — Ad hoc reviewer for work on AI, learning, and human–AI interaction, including completed reviews for Computers in Human Behavior: Artificial Humans.
AI as a Co-Regulator: Relational Design for Strengthening Self-Regulated Learning
A theoretical framework for understanding how AI can support learners as they plan, monitor, reflect, and adapt without displacing their agency, judgment, or growth.
Legitimacy Under Algorithmic Authority: A Relational Diagnostic of Leadership Education in AI-Mediated Contexts
A relational diagnostic of algorithmic authority in leadership education, introducing the SCIS framework for examining how legitimacy is configured, misaligned, or strained across surface, cultural, institutional, and systemic domains.
Evaluating Epistemic Guardrails in AI Reading Assistants: A Behavioral Audit of a Minimal Prototype
A behavioral audit of a minimal AI reading-assistant prototype examining whether epistemic guardrails preserve interpretive agency, text-grounded engagement, and user participation under structured interactional pressure.
Relational Integrity in AI: Preserving Human Agency, Accountability, and Meaning Under Pressure
A diagnostic framework for understanding how AI systems can gradually erode relational integrity through ordinary interaction, shifting role clarity, accountability, authority, and meaning before visible harm appears.
The Relational AI Depth Framework: Relational Depth and Responsibility in Human–AI Engagement
A seven-level framework for understanding how human–AI engagement deepens across interaction contexts, and how responsibility, agency, authorship, and relational risk scale as AI systems become more influential in human thinking, work, and co-creation.
How We Invite AI to Participate: Metaphor and the Implicit Assignment of Authority
A conceptual essay examining how metaphors such as tool, assistant, tutor, mentor, or co-creator structure AI participation, shape role boundaries, and implicitly assign authority in human work.
AI as a Generative Pathway System: How Generative AI Reshapes Possibility, Continuation, and Human Agency
A conceptual framework for understanding generative AI as a pathway-shaping system that changes what people perceive as possible, available, continuable, and actionable over time.
The Geometry of Human Participation: The Structural Conditions of Legitimacy in AI-Mediated Systems
A structural account of legitimacy in AI-mediated systems, arguing that meaning, evaluation, and obligation must remain visibly human-borne for participation to stay contestable and accountable.
Explore the complete writing and research record.
The homepage highlights selected public work. For the full record of publications, preprints, manuscripts, proceedings, reports, and public scholarship, visit the dedicated Writing & Publications page.
Engagements
Teaching, facilitation, and public engagement.
I share this work through learning design, facilitated dialogue, public scholarship, and collaborative sessions that help groups surface tradeoffs, clarify responsibilities, and imagine more trustworthy AI futures together.
Learning design
Courses, workshops, and learning experiences on AI, agency, ethics, responsible innovation, and future-readiness.
Talks and facilitated dialogue
Sessions for surfacing tensions, clarifying responsibilities, and helping teams reason together before decisions harden.
Public scholarship and service
Writing, review, governance, and public-interest contributions that strengthen accountability around emerging technology.
Principles
Principles for work that earns trust.
These commitments guide how I approach technology, strategy, and collaboration: by grounding decisions in context, making tensions discussable, and treating impact as something to steward over time.
01
Begin with relationship and context.
I start by understanding the people, communities, institutions, and conditions around the work, so strategy responds to lived context rather than abstract use cases.
02
Surface tradeoffs before decisions harden.
I name assumptions, risks, tensions, and open questions early, making room for clearer judgment and more accountable choices while there is still time to change direction.
03
Design for stewardship beyond launch.
I treat launch as the beginning of responsibility, not the end of design, and plan for care, governance, adaptation, and long-term accountability.
Responsible Direction / Human Agency / Long-Term Stewardship
How questions become direction.
My process helps turn complex questions into responsible direction by clarifying context, surfacing tensions, and translating insight into systems that can be tested, adapted, and cared for over time.
Frame the responsibility
Define what is at stake: the affected people, intended outcomes, constraints, and values that should shape the work.
Explore the tensions
Examine competing pressures, adoption barriers, ethical questions, and choices that could lead the work in different directions.
Shape the system
Turn insight into frameworks, prototypes, narratives, policies, or practices that can be tested, governed, and refined.
Guiding thesis
“Responsible innovation begins with what we build, but its deeper test is the relationships, responsibilities, and futures our work makes possible.”
About Matthew
I work across research, design, and strategy to protect human agency and public good in AI-enabled systems. As a conceptual researcher and framework builder, I develop relational models, methods, and strategic artifacts that help people, institutions, and communities move from possibility to accountable action and long-term care.
Formal publications may use the full author name: Matthew Christian Agustin.
AI Governance
Innovation Strategy
Relational Design
Public Good
Ways to collaborate
Where this work can contribute.
I collaborate on research, design, strategy, and public-interest efforts that need grounded inquiry, relational accountability, and responsible direction before decisions harden.
Responsible AI strategy
Clarifying governance, adoption, implementation, and accountability questions before AI-enabled systems scale.
Research and publication collaboration
Developing manuscripts, frameworks, and research artifacts across AI, learning, governance, ethics, and design practice.
Frameworks and strategic artifacts
Turning complex concerns into decision frameworks, methods, narratives, and tools that support accountable action.
Learning design and facilitated dialogue
Designing courses, workshops, and reflective learning experiences that build AI literacy, future-readiness, and responsible judgment in context.
Fieldwork and community inquiry
Learning from affected people, communities, and institutions while there is still room to shape design and implementation choices with care.
Reflective tools for agency
Creating reflective practices and human–AI concepts that support purpose, judgment, learning, and long-term responsibility.
Support
Support open research, learning, and prototype work.
Much of this work is public-facing, volunteer-led, and developed outside formal funding cycles. The longer-term aim is to strengthen the judgment, agency, accountability, and public value people need to navigate AI-mediated life well. Modest support can help cover practical costs such as conference travel, journal publication fees, platform subscriptions, and maintaining open research, learning materials, and responsible AI prototypes.
Contact
Working on something that needs careful imagination?
Share a little context about what you are researching, designing, building, governing, or learning to steward with care.