Fundamentals of Artificial Intelligence
Currently catalogued as Artificial Intelligence
Artificial intelligence is this course's subject, but industry practice has moved from building single models to specifying, orchestrating, and verifying autonomous agents that call tools and retrieve their own context. The course keeps its theoretical and philosophical core while adding the agentic layer and the evaluation and accountability discipline that now governs professional AI work.
Current description → proposed description
Applied Artificial intelligence investigates the concepts of intelligence, both human and machine, and the nature of information, its origin, description, and transmission. This course focuses on building a theoretical foundation to support the incorporation of artificial intelligence into useful applications. Included are such topics as the ethics of artificial intelligence, machine learning, language processing, expert systems, and automated planning. The nature of human intelligence and the limits of machine intelligence will be treated from a scientific, philosophical, and computational perspective.
Fundamentals of Artificial intelligence investigates the concepts of intelligence, both human and machine, and the nature of information, its origin, description, and transmission. This course focuses on building a theoretical foundation to support the incorporation of artificial intelligence into useful applications. Included are such topics as the ethics of artificial intelligence, machine learning, language processing, expert systems, and automated planning. The nature of human intelligence and the limits of machine intelligence will be treated from a scientific, philosophical, and computational perspective. Building on that foundation, students also examine contemporary agentic systems, including tool-calling and retrieval-augmented architectures, evaluation harnesses, guardrails, and human-in-the-loop escalation, so that they can specify, verify, and take responsibility for work produced by autonomous AI agents.
AppliedAIF2F_Proposal.xlsx), where it appears as “Fundamentals of Artificial Intelligence”.
Workbook lists this as a Course Change BCI (CSC 3400).
What changes
- Retitled 'Fundamentals of Artificial Intelligence' to pair with CSC 4410
- Adds agentic architectures: tool calling, retrieval augmentation, human-in-the-loop escalation
- Adds evaluation harnesses and rubric-based judging as first-class skills
- Adds subgroup-disaggregated error analysis for bias detection
- Adds accountability and verification for machine-generated work
6 proposed outcomes, mapped to 16 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 foundational artificial intelligence methods, including informed state-space search, knowledge representation and inference, probabilistic reasoning, and supervised learning, in working programs that solve a stated problem, justifying the chosen method against at least one alternative and tracing each output back to the underlying algorithm and training data.
Justifying the chosen method against at least one alternative for a stated problem is the act of proposing and evaluating an AI-driven solution rather than merely executing a known algorithm (PLO 4.2), and tracing each output back to the algorithm and training data that produced it is direct evidence of interpreting and explaining AI data outcomes (PLO 6.1).
Students will be able to construct an agentic AI application that combines retrieval-augmented generation, tool calling, and explicit human-in-the-loop escalation to address an authentic human or community need within a stated cost and latency budget.
Targeting an authentic human or community need is service through technology (PLO 2.1); holding the agent to a stated cost and latency budget is concrete stewardship of AI compute resources (PLO 2.3); and designing an explicit human-in-the-loop escalation path is what makes the application human-centered rather than merely automated (PLO 3.2).
Students will be able to evaluate the behavior of machine learning models and AI agents using held-out benchmarks, automated evaluation harnesses, and rubric-based judging, including error analysis disaggregated by demographic and cultural subgroup.
Disaggregating error by demographic and cultural subgroup is the operational mechanism for detecting bias against varied perspectives (PLO 2.2); judging a system on measured outcomes rather than impression is critical analysis of data-driven outcomes and their societal impact (PLO 4.1); and pairing numeric benchmarks with rubric-based judging is exactly the combination of statistical and qualitative analysis called for (PLO 6.2).
Students will be able to differentiate human intelligence from machine intelligence by integrating scientific, philosophical, computational, and Christian theological accounts of mind, knowledge, and personhood.
Requiring a Christian theological account of mind, knowledge, and personhood alongside the scientific, philosophical, and computational ones obliges students to reason from biblical teaching about what machines are and are not (PLO 1.1), and holding all four accounts together on a single question combines knowledge from ethics, data science, and theology in the interdisciplinary way the PLO names (PLO 3.1).
Students will be able to articulate, as a contributing member of an interdisciplinary project team that must reconcile competing technical and ethical priorities, a transparent written and oral account of an AI system's design, data provenance, and known limitations.
Delivering the account in both written and oral form while disclosing known limitations is transparent communication with integrity (PLO 5.1); reconciling competing technical and ethical priorities with teammates is the scorable act of navigating diverse views in teamwork (PLO 5.2); and an interdisciplinary team weighing system design against data provenance and limitations is precisely the balancing of technical and ethical dimensions the collaboration PLO describes (PLO 3.3).
Students will be able to critique AI-generated artifacts such as code, plans, summaries, and citations against verifiable primary sources, documenting the chain of human accountability for output the student did not personally author, and evaluating that accountability as an exercise of Christian vocation and stewardship.
Documenting a chain of human accountability for machine-produced work and weighing it against Christian commitments is the reflection on how Christian values guide decisions about transparency and accountability that the PLO requires (PLO 1.2); evaluating that accountability as an exercise of Christian vocation and stewardship is the student assessing their own role as a steward of AI and their work as a calling (PLO 1.3); and repeatedly checking machine output against primary sources and adjusting one's own reliance on it within that Christian framing is continuous, faith-informed self-assessment of one's problem-solving approach (PLO 4.3).
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 1.1 Understanding Biblical Foundations | I | covered |
| PLO 1.2 Faith in Professional Practice | I | covered |
| PLO 1.3 Vocation and Purpose | I | covered |
| PLO 2.1 Service through Technology | I | covered |
| PLO 2.2 Engagement with Diversity | I | covered |
| PLO 2.3 Stewardship of AI Resources | I | covered |
| PLO 3.1 Interdisciplinary Problem-Solving | D | covered |
| PLO 3.2 Real-World AI Applications | D | covered |
| PLO 3.3 Collaboration in AI Projects | I | covered |
| PLO 4.1 Data and Ethical Analysis | I,D | covered |
| PLO 4.2 Innovative AI Solutions | I | covered |
| PLO 4.3 Reflective Decision-Making | I | covered |
| PLO 5.1 Clear and Ethical Communication | I | covered |
| PLO 5.2 Interpersonal Skills in AI Collaboration | I | covered |
| PLO 6.1 Data Interpretation in AI | I,D | covered |
| PLO 6.2 Quantitative and Qualitative Analysis | I,D | covered |
I = Introduced · D = Developed · AE = Assessed at Exit