Internship
Interns now enter workplaces where AI agents draft, review, and operate software, so the professional skill being supervised has shifted toward specifying work, verifying machine-generated output, and answering for code the intern did not type. Placements that restrict AI tooling are equally instructive, since the intern must then justify the verification and attribution practices used in its place.
Current description → proposed description
This course consists of supervised work in a given area of computer science in an industrial or business setting. The topic of the internship is determined in conjunction with the responsible faculty, the on-site supervisor, and the student.
This course consists of supervised work in a given area of computer science in an industrial, business, or nonprofit setting, with the topic determined in conjunction with the responsible faculty member, the on-site supervisor, and the student. Because AI coding assistants and agentic tools are now ordinary parts of production workflow, the placement serves as sustained practice in professional accountability for work the student did not personally type. A learning agreement filed at the outset records the host organization's policy on AI tool use, disclosure, confidentiality, and data handling, and fixes which outcomes the student will document at the credit level elected: interdisciplinary collaboration, reflective self-assessment, and the final report are required of every student, with the remaining outcomes required at two or three credits. Periodic reflective journals, an on-site supervisor evaluation, and a final written and oral report complete the experience, connecting daily technical work to professional ethics and to the student's understanding of vocation. Credit is variable (1-3) by agreement with the responsible faculty member.
What changes
- Nonprofit placements named alongside industry and business
- Host organization's AI tool-use, disclosure, and data-handling policy recorded in the learning agreement
- Verification of AI-assisted output before submission, with an equivalent evidence path for AI-restricted placements
- Outcome requirements scaled to the elected credit level, protecting both exit-assessment outcomes at 1 credit
- Explicit vocation and professional-ethics framing of the placement
6 proposed outcomes, mapped to 10 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 implement assigned technical deliverables that satisfy requirements they elicited from stakeholders outside computer science, translating those stakeholders' non-technical constraints - regulatory, contractual, accessibility, or communication - into technical decisions that meet the host organization's standards for correctness, documentation, and review.
The student personally elicits the requirements and converts legal, contractual, accessibility, and communication constraints into technical decisions, which is the application of skills from law, philosophy, and communication to human-centered development that PLO 3.2 names.
Students will be able to evaluate competing technical and ethical obligations arising on an interdisciplinary workplace team, documenting in the final report at least one instance in which they balanced that tension with supervisors, clients, or colleagues outside computer science and justifying the resolution they reached.
The documented instance of balancing technical against ethical obligations inside a real interdisciplinary team is exactly the participation-and-balancing evidence PLO 3.3 assesses at exit, and reaching and defending that resolution across differing professional viewpoints is the teamwork PLO 5.2 describes.
Students will be able to evaluate any AI-generated or AI-assisted work product before submitting it as their own - verifying correctness, provenance, and licensing, judging whether its behavior is equitable for the people it affects, and confirming compliance with the host's disclosure and data-handling policy - or, where the host restricts such tools, evaluate that restriction and document the verification, attribution, and equity-review practices used in its place.
Verifying the correctness and provenance of machine-generated output before it ships is the critical analysis of AI-driven results and their societal implications in PLO 4.1, and judging whether that output's behavior is equitable for the people it affects is the fairness and moral-impact assessment PLO 6.3 requires.
Students will be able to articulate the scope, methods, and results of their internship work in a written report and an oral presentation addressed to faculty, peers, and non-specialist audiences.
The written report and oral presentation are the transparent written and spoken communication of PLO 5.1, and addressing non-specialist audiences is the translation work named in PLO 5.3.
Students will be able to critique their own professional performance through structured reflection, identifying how workplace decisions, including when to rely on, verify, or override AI tools, shaped outcomes and evaluating those decisions against professional standards and their Christian faith commitments.
Recurring self-assessment of decision-making measured against a faith-informed standard is precisely the reflective practice PLO 4.3 assesses at exit in this course, and weighing tool-reliance and disclosure choices against Christian values addresses the transparency and accountability concerns of PLO 1.2.
Students will be able to assess their placement as an expression of Christian vocation, relating the host organization's work and their own contribution to service of neighbor and to their continuing professional calling.
Reading the placement as calling and stewardship, and situating one's own contribution within service to humanity, is the vocational evaluation PLO 1.3 requires.
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 eighteen.
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 3.3 Collaboration in AI Projects | AE | covered |
| PLO 4.3 Reflective Decision-Making | AE | covered |
I = Introduced · D = Developed · AE = Assessed at Exit