New opportunity

Director of Applied AI for Academic Initiatives

College of the Holy Cross · Worcester, United States of America

About this role

The Director of Applied AI for Academic Initiatives leads the College of the Holy Cross’s applied use of AI and emerging technologies across research, teaching, learning, and student development. Based in Worcester, Massachusetts, the position is eligible for hybrid work and has a 40-hour weekly, 12-month schedule.

Responsibilities

Partner with faculty across disciplines to assess where AI, including agentic systems, can advance research. Design feasible pilots and advise on research computing, tools, data handling, provenance, and reproducibility. Support inquiry into AI’s effects on student development and co-curricular life.
Help establish and lead an AI Exploration Lab with a designee of the Office of the Provost. Mentor student-led research from question selection and faculty pairing through methods and work suitable for presentation or publication. Curate the Lab’s models and tools, and work with Information Technology Services to prototype promising projects.
Identify relevant grants and fellowships; help faculty develop technical narratives, architectures, data management plans, and computing budgets. Build relationships with peer institutions, funders, consortia, and vendors.
Partner with learning design and inclusive-teaching colleagues on courses and assessments, including accessibility, differences in student access, and situations where limiting AI supports desirable difficulty. Contribute to AI governance and advise on procurement, privacy, academic integrity, research data, and when to invest in, pilot, or avoid emerging technologies.

Qualifications

A master’s degree in learning design, instructional technology, education, a relevant discipline, or a closely related area; five or more years of relevant professional experience, including higher education experience.
Demonstrated learning or instructional design expertise, substantive faculty partnership, research support, and experience mentoring student research or aptitude for it. Requires working knowledge of AI tools, including large language models and agentic systems; familiarity with learning science; experience building a new program, lab, or practice; and excellent communication skills.
A doctorate, familiarity with grant-seeking or sponsored research support, and liberal arts experience are preferred.

The salary range is $137,000–$145,000, commensurate with education, experience, and qualifications. An updated resume and cover letter are required for the internal application.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

Applied artificial intelligenceLarge language modelsAgentic systemsLearning designInstructional designFaculty research supportStudent research mentoringResearch computingData managementResearch reproducibilityGrant proposal developmentLearning scienceCourse designAssessment designAI governanceAI tool evaluationAccessibilityAcademic integrityCross-disciplinary collaborationWritten communicationVerbal communication

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Turn faculty research questions into credible AI pilots
The posting calls for cross-disciplinary faculty partnership, including assessing agentic systems in laboratory and computational research. The Director must judge feasibility and advise on computing, data handling, provenance, and reproducibility.
Build the AI Exploration Lab around student research
With a Provost’s Office designee, the Director will shape the Lab’s purpose and operating model, mentor student-led work from question selection through presentation or publication, curate tools, and work with Information Technology Services on prototypes.
Strengthen teaching without defaulting to AI use
In partnership with Learning Design and the Center for Inclusive Excellence in Teaching, the Director will advise on rigorous courses and assessments, accessibility and unequal student access, and when constraining AI preserves desirable difficulty.
Guide responsible adoption and technical handoffs
The Director contributes to AI governance and advises on procurement, privacy, academic integrity, and research data. The role also coordinates with AI engineers, Data Architecture, Enterprise Systems, and Information Security so prototypes can be secure and supportable.
Connect faculty work to external funding
The Director identifies relevant grants and fellowships, helps develop technical narratives, architectures, data management plans, and realistic computing budgets, and builds external relationships in coordination with sponsored research and advancement colleagues.

Likely challenges to think through

Distinguishing useful research applications from attractive demos
Preparation hypothesis: Across unfamiliar disciplines, a promising AI demonstration may not answer a faculty member’s research question reliably or reproducibly. Prepare a way to define the scholarly aim, compare AI with non-AI approaches, and set feasibility and provenance checks before proposing a pilot.
Balancing AI access with durable learning
Preparation hypothesis: Broad AI availability may help some students while reducing practice or widening access gaps for others. Prepare an approach for deciding when to permit, constrain, or remove AI in an assessment, with attention to accessibility, retrieval practice, and desirable difficulty.
Moving experiments toward supportable systems
Preparation hypothesis: Rapid Lab prototyping may conflict with privacy, security, procurement, or ongoing support needs. Prepare a staged decision framework for what stays an experiment, what merits engineering review, and what should be paused or avoided.
Giving student projects enough structure without narrowing inquiry
Preparation hypothesis: Student-led research needs room for original questions, yet viable scope, faculty mentorship, appropriate tools, and presentation-quality methods take coordination. Prepare an example framework for scoping and mentoring projects while preserving student ownership.

Practice your answers

Technical practice

How would you decide whether an agentic system could advance a faculty research question?

What to coverSuggested answer focus: Start with the faculty member’s research aim, then compare an agentic approach with a simpler tool or non-AI method. Describe a bounded pilot with criteria for feasibility, scholarly value, error detection, and a decision to continue or stop.

How would you make AI-assisted research reproducible?

What to coverSuggested answer focus: Explain how you would document data sources, model and tool versions, prompts or workflows, human decisions, and changes to outputs. Address data-handling constraints and what another researcher would need to inspect or repeat the work.

How would you structure the AI Exploration Lab’s student-led research work?

What to coverSuggested answer focus: Cover viable question selection, faculty-mentor pairing, suitable tooling, methods, and standards for presentation or publication. Explain how an operating model and terms of responsible experimentation would give students room to explore without compromising research quality.

How would you evaluate models and tools for the Lab?

What to coverSuggested answer focus: Compare fitness for specific research or learning tasks rather than treating one model as universally best. Discuss limitations, reliability, access, data considerations, and when a tool should not be used.

How would you study AI’s effects on student development and co-curricular life?

What to coverSuggested answer focus: Propose a focused research question and a feasible way to gather and interpret evidence with faculty or staff. Distinguish observed effects from assumptions, and consider whether experiences differ among students.

When should a course assessment permit, constrain, or exclude AI assistance?

What to coverSuggested answer focus: Relate the choice to learning goals and the roles of retrieval practice, spacing, and desirable difficulty in durable learning. Explain how faculty could preserve rigorous evidence of student learning when AI is widely available.

How would you assess whether an AI-supported learning activity is inclusive?

What to coverSuggested answer focus: Examine accessibility and differences in students’ access to tools, alongside whether assistance widens or narrows gaps. Describe how you would work with learning-design and inclusive-teaching colleagues to adjust the activity based on those findings.

How would you advise on adopting an emerging AI tool?

What to coverSuggested answer focus: Distinguish investment, a limited pilot, and restraint using educational or research value and supportability. Include privacy, accessibility, academic integrity, research data, and procurement considerations in the recommendation.

How would you support the technical portion of an AI-related grant proposal?

What to coverSuggested answer focus: Show how the proposed approach, architecture, data management plan, and computing budget would match the faculty research aims and funding opportunity. Note where coordination with sponsored research and advancement colleagues matters.

How would you move a faculty or student AI idea into a supportable prototype?

What to coverSuggested answer focus: Define the smallest useful prototype and what it must demonstrate before further work. Explain how you would collaborate with AI Solutions Engineers and consult Data Architecture, Enterprise Systems, and Information Security on practical constraints.

Behavioural practice

Tell me about a time you partnered with someone outside your discipline on a research problem.

What to coverSuggested answer focus: Give the situation, how you learned the researcher’s aims and translated technical options, and the result. Make clear how the partnership shaped the work rather than merely supplying a tool.

Describe a time you helped build a new program, lab, or practice.

What to coverSuggested answer focus: Explain the starting need, the decisions you owned, and how you established a workable purpose or operating model with others. State the result and what you changed after learning from early use.

Tell me about a time you mentored a student through an uncertain research project.

What to coverSuggested answer focus: Describe the initial question, your actions to improve scope or method, and how you supported appropriate independence. Give the result without overstating what the project established.

Describe a time you coordinated colleagues with different priorities to deliver a technical or educational project.

What to coverSuggested answer focus: Identify the situation and each group’s concerns, then explain how you resolved trade-offs and kept decisions clear. Report the outcome and your specific contribution.

Tell me about a time you recommended against using a promising technology.

What to coverSuggested answer focus: Explain the context, the evidence and risks you weighed, and how you communicated your recommendation respectfully. Describe the result, including any alternative or limited experiment you proposed.

Sharpen your knowledge

Topics to prepare

Where AI helps research
The posting calls for critical judgment about large language models and agentic systems across disciplines. For revision, compare a literature-synthesis use case with an experimental-design use case; identify what AI could contribute, how a faculty member would check the results, and when not to use it.
Learning science and AI-assisted assessment
The posting names retrieval practice, spacing, and desirable difficulty, and asks when limiting AI supports learning. For revision, explain how each principle would affect one assignment and where AI assistance would undermine its learning goal.
Inclusive access to AI in courses
The role examines accessibility, differences in student access, and whether AI widens or narrows gaps. For revision, take a hypothetical AI-assisted assignment and identify two access barriers and a way to preserve a rigorous learning experience.
Integrity of AI-assisted scholarship
The Director advises on research computing, data handling, provenance, and reproducibility. For revision, trace a hypothetical research workflow from source data to interpretation, noting what would need to be recorded for others to inspect or repeat the work.
Institutional AI governance
The posting calls for advice on procurement, privacy, accessibility, academic integrity, and research data. For revision, apply those considerations to a proposed AI tool and explain whether you would recommend investment, a limited pilot, or restraint.
Purpose of the AI Exploration Lab
The role helps establish the Lab's purpose, operating model, and terms of responsible experimentation, with student-led research central to its work. For revision, outline how a project would move from a student question to faculty mentorship, appropriate tooling, and work suitable for presentation.

Skills to revise

Scope a faculty research pilot
Practice writing a one-page pilot brief for a faculty-proposed AI use: research question, feasible method, computing and data needs, evaluation criteria, and a reason to stop. The posting explicitly asks the Director to design feasible pilots with faculty across disciplines.
Redesign an assignment with faculty
Practice revising one assignment to specify where AI is permitted, constrained, or excluded, and how learning will be assessed. This reflects the role's partnership with learning-design and inclusive-teaching colleagues on rigorous course and assessment design.
Guide a student research project
Practice turning a broad student interest into a viable question, a method, faculty-mentor needs, and a presentation-ready deliverable. The posting makes mentoring and curating student-led research a central Lab responsibility.
Draft grant-support materials
Practice a short technical narrative, architecture outline, data management plan, and itemized computing-budget rationale for a hypothetical faculty proposal. The posting assigns these proposal-support tasks; familiarity with grant-seeking is preferred.
Translate an idea for an ITS prototype
Practice explaining a faculty or student idea to both a nontechnical partner and an engineering colleague, including its limits, data considerations, and what a prototype would test. The role works with AI Solutions Engineers and other Information Technology Services colleagues to turn promising ideas into supportable systems.

Your preparation checklist

Map evidence to the role
Prepare an evidence sheet with specific examples, where available, of higher education work, faculty partnership, learning design, research support, student mentoring, and building a new program or practice. Note outcomes and your own contribution.
Build a faculty research pilot walkthrough
Prepare a real example or clearly labeled hypothetical pilot for a faculty research question. Show how you would decide whether an LLM or agentic system helps, define success measures, and address computing, data handling, provenance, and reproducibility—including a reason not to use AI.
Sketch an AI Exploration Lab operating model
Draft a one-page proposal for the Lab’s purpose, responsible experimentation rules, tool-selection criteria, and path from student question and faculty pairing to a presentable result. Identify where the Provost’s designee and Information Technology Services would partner.
Redesign an assignment for thoughtful AI use
Prepare a sample assignment and assessment rationale for discussion with learning design and inclusive-teaching colleagues. Address access and accessibility, rigorous evaluation, and when retrieval practice or desirable difficulty warrants limiting AI assistance.
Practice a tool-governance decision
Create a short decision memo comparing pilot, investment, and restraint for a hypothetical AI tool. Include privacy, research data, accessibility, academic integrity, procurement, and the Information Technology Services colleagues needed to assess supportability.
Outline funding support for a pilot
Draft a sample proposal-support outline covering a technical narrative, architecture, data management plan, and realistic computing-budget categories. Treat it as an interview exercise, not as an identified grant opportunity.
Prepare application materials and leadership stories
The posting requests an updated resume and cover letter for its internal application, with a reflection on alignment with the College’s mission. Prepare concise stories showing how you explained technical choices across disciplines and helped build something new; use only examples you can substantiate.
Bring questions about priorities and partnerships
Ask how the College will set the Lab’s initial research priorities, divide decisions among the Provost’s office, faculty, learning design, and Information Technology Services, and judge when a pilot is ready to scale or should stop.

Availability last checked: Nov 4, 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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