Applied Research Practicum
In a practicum that mirrors 2026 professional practice, the student's contribution has shifted from writing most of the code to specifying, orchestrating, reviewing, and evaluating work produced by AI coding agents and tool-calling language models. The course therefore holds students accountable for machine-produced deliverables — their verification, their disclosure to the sponsor, and their behavior after deployment — rather than only for their own keystrokes.
Current description → proposed description
This project-based course provides students with the opportunity to apply advanced computing knowledge and skills to real-world challenges through collaboration with faculty-led research initiatives, industry partners, or interdisciplinary teams. Students will engage in the full lifecycle of a substantial computing project while working in a professional, team-oriented environment. Emphasis is placed on producing work that meets professional standards for quality, scalability, security, and usability, while also demonstrating ethical integrity and consideration of societal impact. Throughout the course, students will refine their skills in technical communication, project management, and collaborative problem-solving. Deliverables include regular progress reports, stakeholder presentations, technical documentation, and a final project showcase. Successful completion of this course will equip students with practical experience, a professional portfolio piece, and potential industry connections that may lead to future employment or research opportunities.
This project-based course provides students with the opportunity to apply advanced computing and artificial intelligence knowledge and skills to real-world challenges through collaboration with faculty-led research initiatives, industry partners, or interdisciplinary teams. Students will engage in the full lifecycle of a substantial computing project while working in a professional, team-oriented environment in which AI coding agents and tool-calling language models are ordinary instruments of the work and the student remains accountable for everything merged or deployed. Emphasis is placed on producing work that meets professional standards for quality, scalability, security, and usability, while also demonstrating ethical integrity and consideration of societal impact. Each project carries an evaluation harness, guardrails, cost and latency budgets, and post-deployment monitoring proportionate to its stakes. Throughout the course, students will refine their skills in technical communication, project management, and collaborative problem-solving. Deliverables include regular progress reports, stakeholder presentations, technical documentation, a disclosed record of AI-assisted contribution and how it was verified, and a final project showcase. Successful completion of this course will equip students with practical experience, a professional portfolio piece, and potential industry connections that may lead to future employment or research opportunities.
What changes
- AI coding agents and tool-calling LLMs treated as ordinary instruments of project work
- Student accountability for every merged or deployed AI-produced artifact
- Evaluation harnesses, guardrails, and cost/latency budgets sized to project stakes
- Disclosed record of AI-assisted contribution and how it was verified
- Post-deployment monitoring, provenance, and human-in-the-loop escalation as deliverables
6 proposed outcomes, mapped to 7 program outcomes
Each outcome below is written to be observable and assessable, and each is mapped to the program learning outcomes for which it produces evidence.
Students will be able to construct a project specification and delivery plan for a sponsored applied computing problem that defines scope, acceptance criteria, data sources, evaluation measures, and the deployment architecture, API integration points, and cost and latency budgets for any AI components operating in the sponsor's environment.
Specifying the deployment architecture, API integration points, and cost and latency budgets for LLM components that will run in a sponsor's production environment is exactly the structured deployment of LLMs through APIs in enterprise environments that PLO 6.1 requires.
Students will be able to implement a substantial project deliverable using AI coding agents and tool-calling language model components, directing them through explicit specifications, structured prompts, retrieval and tool interfaces, and review gates that keep the student accountable for every merged result.
Directing agents through explicit specifications and structured prompts to obtain correctly formatted, aligned output is the prompt engineering PLO 4.2 describes, and building the deliverable on retrieval, embeddings, and tool/API interfaces inside a sponsor's production setting is the industrial use of LLMs named in PLO 6.1.
Students will be able to refine the project's AI-assisted components based on diagnostic results from an evaluation harness of held-out cases, task-level metrics, and LLM-as-judge scoring with human adjudication, documenting the failure modes each iteration addressed.
Building the harness, reading its metrics as diagnostics, and iterating on the system while documenting the failure modes resolved is precisely the assess-then-refine cycle PLO 6.2 specifies.
Students will be able to justify the mitigations, human-in-the-loop escalation points, and data and model provenance controls built into the delivered system in light of its bias, privacy, data-licensing, prompt-injection, and tool-supply-chain exposures.
Defending concrete mitigations, escalation points, and provenance controls against named bias, privacy, licensing, and injection exposures is implementation with ethical and legal integrity, and the workbook states this same outcome twice — once under Christian Faith as PLO 1.1 and once under Integrated Disciplinary Knowledge as PLO 3.2 — so this CLO supports both identically.
Students will be able to communicate project progress, results, and limitations to technical and non-technical stakeholders through regular progress reports, design reviews, technical documentation, and a final showcase that discloses where AI agents contributed and how that contribution was verified.
Progress reports, design reviews, documentation, and a showcase addressed to mixed audiences, including honest disclosure of limitations and of AI-assisted contribution, is the clear and responsible communication to technical and non-technical audiences that PLO 5.1 requires.
Students will be able to justify their professional conduct across the practicum as Christian vocation, articulating how obligations to the sponsor, honesty about the limits of AI-produced work, and care for those affected by the deployed system shaped the decisions they made.
Accounting for sponsor obligations, honesty about machine-produced work, and care for those affected as expressions of work done in service of the neighbor is the vocational articulation guided by a Christian worldview that PLO 1.2 names.
Program outcomes this course reaches
Filled cells are program learning outcomes with at least one supporting course learning outcome in this course. Sparse coverage is expected — no single course carries all twelve.
The workbook maps this course too
The proposal workbook's assessment map already assigns program learning outcomes to this course. The outcomes above were written to cover it.
| Program outcome | Workbook level | In this draft |
|---|---|---|
| PLO 1.1 Ethical and Legal Integrity in AI | D | covered |
| PLO 1.2 Vocation and Christian Worldview | I | covered |
| PLO 3.2 Ethical and Legal Integrity in AI | D | covered |
| PLO 4.2 Prompt Engineering for LLMs | I | covered |
| PLO 5.1 Communicating AI to Any Audience | D | covered |
| PLO 6.1 LLMs in Industrial Settings | I | covered |
| PLO 6.2 Model Evaluation and Refinement | I | covered |
I = Introduced · D = Developed · AE = Assessed at Exit