Forming Intelligence — Framework Outline v0.1

Purpose of this Document

The position statement now in draft — Forming Intelligence: A Saint Joseph’s University Position on Artificial Intelligence and the Care of Our Common Home — advances a coherent argument at one altitude: the magisterial-strategic register that the Board of Trustees, the cabinet, and the institution’s closest constituencies need to hear in the President’s voice. It is the right document at the right altitude. It is not, on its own, the full institutional position.

The timing is not incidental. With Magnifica Humanitas (15 May 2026), Pope Leo XIV has named artificial intelligence as a defining question for the Church and the world — and named Catholic universities explicitly as central to forming people capable of meeting it well. The institution’s position is grounded in that encyclical as its environmental commitments are grounded in Laudato Si’.

A cohesive, unified institutional position on artificial intelligence requires the full conceptual landscape to be mapped, governed, and operationalized across every domain in which AI touches the work of the university. This document is that map. It is the working table-of-contents for the comprehensive position document that the Guiding Coalition will develop over the months ahead — and it is the scoping instrument by which the Coalition determines where it must lead, where it must coordinate, and where it must defer.

The architecture is offered at two altitudes. Seven Pillars compress the working architecture into the communicable institutional vocabulary the university will carry in its public voice. Ten Domains, organized beneath the pillars, name the actual territory on which the Coalition’s work is conducted. The pillars are the institution’s shorthand; the domains are the Coalition’s worksheet.

The position statement currently addresses, in whole or in part, Domains 1, 2, 3, 7, 8, 9, and 10. The remainder are either implicit, gestured at, or absent — which is precisely what a Guiding Coalition exists to resolve.

The Seven Pillars

These seven pillars compress the institution’s working architecture into a vocabulary the university can carry in its public voice — communicable in a slide, durable across audiences, consistent across the institution’s external presentation. Beneath each pillar live one or more of the ten domains in the next section.

Pillar

Mission and Identity

Artificial intelligence must serve the Catholic and Jesuit mission of the university; it must not redefine it. Every commitment the institution makes about AI is anchored in who Saint Joseph’s is, not in what the technology asks of us.

Domains 1, 2

Pillar

Human Formation

AI must strengthen, never substitute for, the relational and developmental work that constitutes a Saint Joseph’s education. Where AI strengthens formation it is welcomed; where it weakens formation it is refused.

Cross-cutting; anchored in Domain 1, with operational expression in Domains 3, 4, 5

Pillar

Academic Integrity and Intellectual Honesty

AI must be integrated into teaching and research with transparency, faculty judgment, and a clear standard of intellectual honesty. Disclosure is not a sanction; it is the condition under which AI use is consistent with the work of a serious scholarly community.

Domains 3, 4

Pillar

Responsible Operations and the Dignity of Work

AI must improve institutional effectiveness without diminishing the dignity of work, the relational character of student support, or the trust of the people the institution serves. Internal labor dignity is the test the institution applies to itself before it applies it to anyone else.

Domains 5, 6, 7

Pillar

Data, Privacy, and Vendor Stewardship

AI adoption must be governed through disciplined data classification, procurement review, and vendor accountability. The institution acts as one buyer, one steward, and one conscience — not as a federation of uncoordinated offices.

Domain 7, with cross-references to 9 on bias and 10 on risk

Pillar

Care for Our Common Home

AI use must be environmentally accountable and proportionate to the mission value it generates. Laudato Si’ is not a compliance frame; it is a constitutive one.

Domain 8

Pillar

Governance Through Discernment

AI decisions must be made through a structured process that includes mission, academic, operational, technical, environmental, and equity considerations. The institution moves as one when it discerns as one.

Domain 10

Equity, access, and inclusion are not a separate pillar by design. The institution’s commitment to them is threaded through every pillar above — algorithmic bias under Pillar 5, access and the digital divide under Pillars 2 and 3, the preferential option for the vulnerable under Pillar 1. Domain 9 in the architecture below catalogs the questions; the pillars locate them in the work where they are actually decided.

The Ten Domains

Beneath the pillars, ten domains name the territory on which the Coalition’s actual work is conducted. Each domain decomposes into the sub-concepts the Coalition must address if the institutional position is to hold together under examination.

1.0

Anthropological and Theological Foundation

What we believe about persons, intelligence, and formation — the ground beneath every other position.

1.1 The nature of human intelligence and the categorical limits of artificial intelligence (the Antiqua et Nova frame)
1.2 The dignity of the person as the non-negotiable boundary condition
1.3 Formation as the irreducible Jesuit core (cura personalis, the Ignatian pedagogical paradigm, the magis)
1.4 Magnifica Humanitas (Leo XIV, 2026) as the magisterial ground of the institution’s AI position — the encyclical that names artificial intelligence directly and Catholic universities explicitly; the position follows it as the university’s environmental stewardship follows Laudato Si’
1.5 Care of our common home as a coextensive obligation (Laudato Si’, Laudate Deum)
1.6 The Catholic social-doctrine inheritance the institution explicitly carries — subsidiarity, solidarity, the common good, the preferential option, and the universal destination of goods extended to data and algorithms
1.7 Convergence with — and distinction from — broader AJCU and Catholic higher-education posture

2.0

Strategic-Institutional Frame

Why a position is required now, and what it accomplishes competitively and mission-wise.

2.1 The Scarcity Inversion thesis — AI commoditizes knowledge; formation conveys wisdom
2.2 The Ignatian Advantage as the institution’s strategic distinctiveness on a 2036 horizon
2.3 The bilingual discipline — every commitment expressible in Ignatian register and in market-strategic register
2.4 Position vs. policy vs. practice — what each document does, and how they relate

3.0

Teaching and Learning

Where AI most directly meets the formational mission — in the classroom and in the experiential learning that surrounds it — and where divisional improvisation has already begun.

3.1 Course-level expectations (syllabus standards, disclosure norms, faculty discretion within framework)
3.2 Academic integrity reconceived — from prohibition language to formation language
3.3 Assessment redesign — what we measure when output is no longer a reliable proxy for learning
3.4 Disciplinary variation — humanities, sciences, business, professional schools, education, health sciences
3.5 AI as content, AI as tool, AI as collaborator — the three modes students must learn to distinguish
3.6 Transformational educational experiences — experiential, community-engaged, and service learning as the formation classroom instruction alone cannot provide, with Philadelphia as the formation laboratory
3.7 Faculty development and pedagogical investment
3.8 First-year experience and curricular sequencing
3.9 Academic freedom within institutional framework — the boundary and the protection

4.0

Research, Scholarship, and Catholic Intellectual Contribution

The university’s contribution as a knowledge-producing institution, not only a knowledge-transmitting one.

4.1 AI as research instrument — disclosure, attribution, data provenance standards
4.2 Human-subjects research with AI-mediated data (IRB implications)
4.3 AI as research subject — the Catholic university’s distinctive scholarly opportunity
4.4 Faculty research integrity in an AI-saturated scholarly environment
4.5 Library, archival, and information-literacy practice

5.0

Student Experience Beyond the Classroom

Formation extends beyond instruction — into experience, service, and relationship, where the developmental work AI cannot perform actually happens.

5.1 Transformational educational experiences as the core of formation beyond the classroom — experiential and community-engaged learning as the irreplaceable site of person-formation
5.2 Service learning and community-engaged formation in Philadelphia — the city as the university’s formation laboratory, where students encounter the persons and questions no model can simulate
5.3 Student conduct, honor, and the redefinition of academic dishonesty
5.4 Academic advising and the human–AI division of labor in student support
5.5 Mental health, counseling, and the question of AI companions in vulnerable populations
5.6 Career formation and workforce readiness in an AI-disrupted labor market
5.7 Co-curricular formation — retreats, ministry, athletics — and AI’s appropriate place in each
5.8 Student data privacy and the dignity of the student as data subject

6.0

People — Faculty, Staff, and the Dignity of Work

The dignity of work — applied inside the institution to faculty and staff, and outward to the labor that built the systems we use.

6.1 Job evolution and role redesign across staff functions
6.2 Reskilling, professional development, and internal mobility commitments
6.3 Labor dignity for staff in commoditizable roles
6.4 Faculty governance and shared governance under AI-accelerated change
6.5 Hiring, screening, and the use of AI in employment decisions
6.6 Performance evaluation, promotion, and tenure considerations
6.7 Labor justice in AI supply chains — training-data labor, content-moderator labor, creator IP

7.0

Operations and Administrative Adoption

The federation-of-uncoordinated-offices problem the position statement names but does not yet fully solve.

7.1 Use-case inventory across cabinet divisions
7.2 Procurement standards and the “one buyer, one steward, one conscience” principle
7.3 Vendor evaluation criteria (environmental, mission-alignment, security, IP)
7.4 Data governance, classification, and information security
7.5 Cost discipline and return-on-investment methodology for AI investments
7.6 Cybersecurity posture under AI-augmented threats

8.0

Environmental and Common-Home Stewardship

The Laudato Si’ obligation operationalized — and named again as a justice issue in Magnifica Humanitas.

8.1 Measurement framework — energy, water, Scope 3 vendor emissions
8.2 Procurement preferences for renewably powered providers
8.3 Annual public reporting cadence and audience
8.4 Integration with the campus sustainability committee and broader climate commitments
8.5 Catholic and higher-education coalition participation
8.6 Threshold-and-refusal protocol — conditions under which a deployment will not proceed

9.0

Equity, Access, and Inclusion

The equity dimension of AI adoption — currently thin in the draft, and foundational for a Jesuit institution in West Philadelphia.

9.1 Algorithmic bias in systems the institution deploys
9.2 Access and the digital divide — the AI literacy gap as the new educational equity question
9.3 Philadelphia community impact and the institution’s accountability to its neighbors
9.4 Race, gender, socioeconomic, disability, and immigration-status considerations

10.0

Governance, Risk, and the Living Policy

How the position remains coherent over time as circumstance changes.

10.1 AI Governance Council composition, authority, and reporting line
10.2 Materiality thresholds for Council and Board review
10.3 Relationship to existing governance bodies (Cabinet, Faculty Senate, Student Government, Board)
10.4 Legal and regulatory risk (FERPA, HIPAA where applicable, copyright, state and federal AI regulation)
10.5 Reputational and mission risk frameworks
10.6 Annual review cadence and the three-year comprehensive revision
10.7 External-relations posture — how the institution speaks about its position to external audiences

A MECE Caveat

The ten domains are offered as mutually exclusive and collectively exhaustive across the territory a unified position must cover. Two productive seams of overlap are acknowledged. Domain 7 (Operations) and Domain 10 (Governance) overlap on procurement review and materiality thresholds; Domain 6 (People) and Domain 9 (Equity, Access, and Inclusion) touch at the boundary between staff dignity and equity in employment decisions. Both seams are addressable through clear ownership assignment in the operating framework rather than through a re-architecting of the categories. The Coalition will resolve ownership at the point of deliberation, not in advance.

How the Coalition Will Use This Framework

This framework serves three concurrent functions. It is the scoping instrument by which the Guiding Coalition determines the boundaries of its own work — deciding where it must lead, where it must coordinate with existing governance bodies, and where it must defer. It is the working table-of-contents for the comprehensive institutional position document that will succeed the present draft. And it is the diagnostic instrument by which the Coalition tracks its own progress — reporting at each session which sub-concepts have been resolved, which remain under deliberation, and which have surfaced new questions not yet anticipated here.

The framework deliberately does not pre-specify the workstreams by which the Coalition organizes its labor. Translating these ten domains into a working portfolio of workstreams — with named outputs, timelines, and ownership — is itself the Coalition’s first substantive deliverable, undertaken in its early sessions once the architecture is internalized. The framework is the territory; the workstreams are the route the Coalition chooses through it.

The framework is itself revisable. Sub-concepts will be added as the work surfaces them. Domains may be split or merged if the Coalition’s discernment yields a cleaner architecture. What does not change is the discipline the framework imposes: every commitment the institution makes about artificial intelligence must be locatable on this map, accountable to a domain, and defensible against the principles in the document’s companion charter.