Internship in IT
Interns now enter IT workplaces where AI coding assistants and tool-calling agents are increasingly ordinary tooling, so the professional expectation has shifted from producing work by hand to specifying, reviewing, verifying, and taking accountability for machine-produced work under employer data-handling and disclosure expectations. The internship therefore adds explicit supervision of how the student uses those tools on site and how their own contribution is documented, without displacing the placement's core purpose of integrating coursework with supervised practice.
Current description → proposed description
The internship provides students with an opportunity to gain valuable practical experience under the guidance of a supervisor/mentor in the work setting, as well as a professor in the academic setting. The goal is to integrate practical work experience with the cumulative knowledge and skills obtained during the students' education. It is expected that students will develop personal, professional and additional academic competencies during the internship. In order to accomplish this, students will need to go beyond the common experiences of a normal employee. Study, reasoning, reflection and theoretical and conceptual exploration will be required for students to develop new skills and knowledge to get the most of the internship experience. All students in the Information Technology program are highly encouraged to obtain relevant work experience in the information technology field before graduation
This supervised field experience places students in an information technology work setting under the joint guidance of a site supervisor and a faculty mentor, so that practical work is integrated with the knowledge and skills built across the graduate program. Students are expected to develop personal, professional, and academic competencies by moving beyond the routine duties of an employee: study, reasoning, reflection, and theoretical and conceptual exploration are required if the placement is to yield new skills and knowledge. Because contemporary information technology work is increasingly carried out alongside AI coding assistants and tool-calling agents, students also consider the host organization's expectations for data handling, confidentiality, and disclosure of machine assistance, whether or not those expectations are set down in formal policy, and take responsibility for verifying any machine-produced work they submit. Requirements are arranged with the responsible faculty member and the site supervisor and may include a learning agreement, an in-depth study of one technical or operational problem encountered on site with a defensible improvement proposal bounded by cost and maintainability, written reports, and a concluding presentation for technical and non-technical listeners. Relevant work experience in the field is strongly encouraged of all students before graduation.
What changes
- Verification standard for AI-assisted work, including where the host has no policy
- Confidentiality-scoped provenance log of the student's own contributions
- One in-depth site problem analyzed under real cost and maintainability constraints
- Results reported to technical readers and to a non-technical decision-maker
- Explicit vocational reflection on service to colleagues and clients
4 proposed outcomes, mapped to 4 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 evaluate the host organization's policies and practices governing AI-assisted work, including the case where no explicit policy exists, justifying the review and verification standard applied before any machine-produced work enters a submitted deliverable and maintaining a provenance log of the student's own contributions, described or redacted to the level the host organization's confidentiality obligations permit, that distinguishes directly authored work from machine-assisted work and records the verification each received.
Judging what employer data may be sent to a model, setting a verification standard where the employer supplies none, and keeping an auditable record of what was machine-assisted and how it was checked is direct evidence of privacy, accountability and transparency in AI use; the workbook states this same outcome twice, under Christian Faith (PLO 1.1) and under Integrated Disciplinary Knowledge (PLO 3.2), so a CLO that supports one supports both.
Students will be able to analyze one technical or operational problem encountered at the placement, synthesizing supervisor feedback, on-site observation, and prior graduate coursework into a defensible improvement proposal bounded by cost, maintainability, and organizational constraint, reported in writing to the site supervisor and faculty mentor and summarized for a non-technical decision-maker at the site.
The same technical finding must be carried to two genuinely different readerships, a technical supervisor and mentor who can check the reasoning and a non-technical decision-maker at the site who needs the consequence without the mechanism, which is the technical and non-technical contrast this outcome specifies.
Students will be able to communicate the substance and the limitations of their internship work to a non-technical stakeholder as well as a technical team, stating assumptions, residual risk, and, where AI assistance was used, how it was verified.
Naming assumptions, residual risk, and any extent of AI assistance for a non-technical audience as well as a technical one is exactly the responsible, audience-adapted communication of results and ethical considerations that this outcome specifies.
Students will be able to appraise the placement as a vocation by examining how specific technical contributions served colleagues, clients, and the wider community, and where professional humility, honesty about the origin of the work submitted, and care for the common good shaped decisions made on site.
The reflection ties the student's own workplace decisions to service of others, professional humility, and the common good, which is the vocational articulation this outcome calls for.
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.