Emerging Technologies in Artificial Intelligence
Emerging AI practice has moved from training and calling models to composing them into agents that retrieve, call tools, and act, so the industry skill set now centers on specification, grounding, evaluation, guardrails, and accountability for machine-generated work. The course keeps its survey-of-the-frontier identity but makes agentic architecture, evaluation harnesses, and operational risk the frontier it surveys.
Current description → proposed description
This course explores cutting-edge advancements in artificial intelligence, focusing on emerging technologies that are transforming industries. Students will examine recent developments in machine learning, natural language processing, computer vision, and generative AI. Emphasis will be placed on understanding the technical foundations, practical applications, and ethical considerations of these innovations. Through hands-on projects and case studies, students will gain insights into how emerging AI technologies are shaping the future of computing and society.
This course explores cutting-edge advancements in artificial intelligence, focusing on emerging technologies that are transforming industries. Students will examine recent developments in machine learning, natural language processing, computer vision, and generative AI. Emphasis will be placed on understanding the technical foundations, practical applications, and ethical considerations of these innovations. Through hands-on projects and case studies, students will gain insights into how emerging AI technologies are shaping the future of computing and society. The course also treats agentic systems: students specify, construct, and evaluate tool-calling agents that use retrieval-augmented generation and the Model Context Protocol, and they weigh guardrails, prompt-injection and supply-chain risk, evaluation harnesses, cost and latency budgets, and human accountability for machine-generated work.
AppliedAIF2F_Proposal.xlsx), where it appears as “Emerging Technologies in Artificial Intelligence”.
Workbook proposes this as NEW course CSC 3450; the catalog already carries this title at CSC 3500.
What changes
- Agentic architectures: tool calling, Model Context Protocol, retrieval-augmented grounding
- Evaluation harnesses, LLM-as-judge, and regression testing as first-class coursework
- Guardrails, prompt injection, and agent supply-chain risk
- Cost, latency, and energy budgets treated as stewardship of AI resources
- Human accountability, provenance, and escalation for machine-generated output
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 differentiate the technical foundations of current emerging AI technologies, including transformer language models, diffusion-based generative models, multimodal vision systems, and retrieval-augmented architectures, comparing their measured behavior on representative benchmark tasks across the data and compute regimes each one requires, and judging which uses each can responsibly support.
Reading and explaining what the benchmark measurements say about each architecture is the interpretation of AI data outcomes named in PLO 6.1; comparing those measured results across data and compute regimes to draw conclusions about the architectures is the quantitative and qualitative pattern analysis of PLO 6.2; and judging from that evidence which uses each architecture can responsibly support is the critical, ethically informed analysis of data-driven outcomes required by PLO 4.1.
Students will be able to construct, as a member of a project team, a working agentic application that combines tool calling, retrieval-augmented grounding, and the Model Context Protocol, with explicit human-in-the-loop escalation points, for a problem defined with a non-technical stakeholder.
Defining the problem with a non-technical stakeholder and designing escalation around it applies communication, legal, and policy considerations to a human-centered application as PLO 3.2 requires; building and defending the agent as a proposed solution is the innovative AI solution work of PLO 4.2; and constructing it as a member of a project team that works with a non-technical stakeholder and must balance the technical build against the human-in-the-loop limits that stakeholder's needs impose is the interdisciplinary team participation described in PLO 3.3.
Students will be able to evaluate an agentic system with a purpose-built harness, including benchmark tasks, LLM-as-judge scoring, regression tests, adversarial prompt-injection probes, and cost, latency, and energy budgets, using the resulting evidence to revise the system.
Building a harness, reading its verdict on one's own system, and revising accordingly is precisely the self-assessing, continuously improving practice PLO 4.3 describes; holding the system to explicit cost, latency, and energy budgets is responsible use of finite compute and the stewardship of AI resources named in PLO 2.3.
Students will be able to critique an emerging AI technology for fairness, bias across cultural and linguistic populations, provenance of training data, and moral consequence, articulating the developer's vocation as a steward accountable for systems that act on behalf of others.
Assessing a technology for fairness and moral consequence is the critical ethical analysis of PLO 6.3; examining bias across cultural and linguistic populations is the respect for varied perspectives required by PLO 2.2; and framing the developer as a steward accountable for delegated action states the calling described in PLO 1.3.
Students will be able to articulate the capabilities, limitations, and residual risks of an AI system in a technical report and in a briefing for a non-specialist audience, disclosing evaluation evidence, failure modes, and which portions of the work were machine-generated.
Reporting evaluation evidence, failure modes, and the provenance of machine-generated work is the transparent, integrity-bearing communication of PLO 5.1; rendering the same system honestly for a non-specialist audience is the translation for broader audiences described in PLO 5.3.
Students will be able to design, as a member of an interdisciplinary team, an emerging-technology solution to a documented community, accessibility, or global development need, integrating technical, ethical, and theological reasoning in the justification of the design.
Directing an emerging technology at a documented community or global need is the compassionate service through technology of PLO 2.1; justifying the design with technical, ethical, and theological reasoning together is the interdisciplinary problem-solving of PLO 3.1; and negotiating that justification inside a team holding diverse views is the collaborative articulation described in PLO 5.2.
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 1.3 Vocation and Purpose | I | covered |
| PLO 2.1 Service through Technology | D | covered |
| PLO 2.2 Engagement with Diversity | D | covered |
| PLO 2.3 Stewardship of AI Resources | D | covered |
| PLO 3.1 Interdisciplinary Problem-Solving | I,D | covered |
| PLO 3.2 Real-World AI Applications | I,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,D | covered |
| PLO 4.3 Reflective Decision-Making | I,D | covered |
| PLO 5.1 Clear and Ethical Communication | I,D | covered |
| PLO 5.2 Interpersonal Skills in AI Collaboration | I | covered |
| PLO 5.3 Engagement with Broader Audiences | I,D | covered |
| PLO 6.1 Data Interpretation in AI | I,D | covered |
| PLO 6.2 Quantitative and Qualitative Analysis | I,D | covered |
| PLO 6.3 Critical Ethical Analysis | I,D | covered |
I = Introduced · D = Developed · AE = Assessed at Exit