AI-UG101 — Foundations of Artificial Intelligence

Institute: Institute for AI Governance, Cybersecurity & Risk Management (IAIGCRM)

Status: Academic model syllabus — v0.1 editorial draft; not accredited or approved by a degree-granting institution

Format: 15 weeks; proposed 3 semester credits; 3 contact hours/week plus independent work

Academic calendar: Instructor to supply dates, location, office hours, accessibility office, institutional policies and official credit-hour equivalence.

Academic standards and responsible AI policy

Students may use AI tools for formative explanation, brainstorming, debugging, or critique where authorized, but must disclose meaningful assistance, verify claims and citations, protect sensitive data, and independently explain submitted work. Fabricated references, undisclosed generated submissions, and submission of code or reasoning students cannot defend violate course expectations. Instructors must offer an equivalent non-AI route when necessary for accessibility, privacy, or equitable access. Research involving human participants, sensitive data, or live systems requires appropriate approvals. All security exercises use owned or expressly authorized environments.

Inclusive teaching and assessment

Use accessible readings, captioned media, reproducible notebooks or non-programming alternatives as appropriate, formative feedback, transparent rubrics, and documented accommodations. Assess conceptual understanding separately from AI-assisted output quality. A final oral defense or live demonstration tests authorship and comprehension.

General grading principles

Each assessment receives a rubric for technical accuracy, evidence, reasoning, reproducibility, ethical awareness, and communication. Late work, attendance, reassessment, academic integrity, and accessibility procedures must be finalized by the adopting institution.

Editorial notes

All readings are indicative; the instructor must verify editions, licensing, links, publication status, and accessibility before adoption. Course credits and prerequisites are proposals rather than institutional approvals.

Course profile

Level: Undergraduate introductory

Prerequisites: No prior AI or programming required; quantitative literacy recommended.

Catalog description: A broad, historically grounded introduction to AI as a computational and social field. Students examine search, knowledge representation, learning, neural networks, generative models, and the limits of AI claims through small, interpretable exercises.

Measurable learning outcomes

1. Explain major historical and conceptual traditions in AI and distinguish symbolic, statistical and generative approaches.
2. Represent and solve elementary search and reasoning problems.
3. Interpret supervised learning, overfitting, evaluation metrics, and data limitations.
4. Explain basic neural-network and transformer concepts without overstating system capabilities.
5. Critically evaluate AI outputs for evidence, uncertainty, bias, privacy, and safety.
6. Communicate a reproducible small AI investigation to a non-specialist audience.

Assessment and weighting

Total: 100%. The capstone must include an individually attributable contribution, documentation and a defense.

Detailed 15-week instructional plan

Each week specifies measurable objectives, instructional activities, laboratory or seminar requirements, security integration and assessment evidence. Faculty must assign exact chapters and articles before delivery.

Week 01 — What is AI? Scope, claims, and scientific method

Weekly objectives: (1) Explain the principal concepts, assumptions and limitations of what is ai? scope, claims, and scientific method. (2) Apply the week's methods in ai taxonomy; diagnostic exercise. (3) Recognize and explain risks, limits, privacy and safe use. (4) Interpret evidence and communicate at least one limitation or trade-off.

Instructional plan: Instructor-led conceptual seminar; guided worked example; structured discussion or peer review; independent reading and preparation.

Laboratory / seminar requirements: AI taxonomy; diagnostic exercise. A structured worksheet, reproducible example or guided simulation. Use only synthetic, public, or explicitly approved data; record procedures and limitations.

Security & governance checkpoint: SEC-02, SEC-03, SEC-04, SEC-07; identify at least one relevant control, potential misuse or failure mode, and proportionate safeguard.

Assessment evidence: Submit a dated lab log or seminar brief with objective, methods, results, references, limitations and reflection.

Reading assignment: Relevant selections from the course reading list, assigned and verified by the instructor before the term.

Week 02 — Intellectual history: logic, Turing, Dartmouth, expert systems

Weekly objectives: (1) Explain the principal concepts, assumptions and limitations of intellectual history: logic, turing, dartmouth, expert systems. (2) Apply the week's methods in annotated timeline and primary-source critique. (3) Recognize and explain risks, limits, privacy and safe use. (4) Interpret evidence and communicate at least one limitation or trade-off.

Instructional plan: Instructor-led conceptual seminar; guided worked example; structured discussion or peer review; independent reading and preparation.

Laboratory / seminar requirements: Annotated timeline and primary-source critique. A structured worksheet, reproducible example or guided simulation. Use only synthetic, public, or explicitly approved data; record procedures and limitations.

Security & governance checkpoint: SEC-02, SEC-03, SEC-04, SEC-07; identify at least one relevant control, potential misuse or failure mode, and proportionate safeguard.

Assessment evidence: Submit a dated lab log or seminar brief with objective, methods, results, references, limitations and reflection.

Reading assignment: Relevant selections from the course reading list, assigned and verified by the instructor before the term.

Week 03 — Problem representation and state spaces

Weekly objectives: (1) Explain the principal concepts, assumptions and limitations of problem representation and state spaces. (2) Apply the week's methods in search problem modeling. (3) Recognize and explain risks, limits, privacy and safe use. (4) Interpret evidence and communicate at least one limitation or trade-off.

Instructional plan: Instructor-led conceptual seminar; guided worked example; structured discussion or peer review; independent reading and preparation.

Laboratory / seminar requirements: Search problem modeling. A structured worksheet, reproducible example or guided simulation. Use only synthetic, public, or explicitly approved data; record procedures and limitations.

Security & governance checkpoint: SEC-02, SEC-03, SEC-04, SEC-07; identify at least one relevant control, potential misuse or failure mode, and proportionate safeguard.

Assessment evidence: Submit a dated lab log or seminar brief with objective, methods, results, references, limitations and reflection.

Reading assignment: Relevant selections from the course reading list, assigned and verified by the instructor before the term.

Week 04 — Search and heuristics

Weekly objectives: (1) Explain the principal concepts, assumptions and limitations of search and heuristics. (2) Apply the week's methods in breadth-first, depth-first and a* worked examples. (3) Recognize and explain risks, limits, privacy and safe use. (4) Interpret evidence and communicate at least one limitation or trade-off.

Instructional plan: Instructor-led conceptual seminar; guided worked example; structured discussion or peer review; independent reading and preparation.

Laboratory / seminar requirements: Breadth-first, depth-first and A* worked examples. A structured worksheet, reproducible example or guided simulation. Use only synthetic, public, or explicitly approved data; record procedures and limitations.

Security & governance checkpoint: SEC-02, SEC-03, SEC-04, SEC-07; identify at least one relevant control, potential misuse or failure mode, and proportionate safeguard.

Assessment evidence: Submit a dated lab log or seminar brief with objective, methods, results, references, limitations and reflection.

Reading assignment: Relevant selections from the course reading list, assigned and verified by the instructor before the term.

Week 05 — Logic, knowledge representation and inference

Weekly objectives: (1) Explain the principal concepts, assumptions and limitations of logic, knowledge representation and inference. (2) Apply the week's methods in rule-base design and limits. (3) Recognize and explain risks, limits, privacy and safe use. (4) Interpret evidence and communicate at least one limitation or trade-off.

Instructional plan: Instructor-led conceptual seminar; guided worked example; structured discussion or peer review; independent reading and preparation.

Laboratory / seminar requirements: Rule-base design and limits. A structured worksheet, reproducible example or guided simulation. Use only synthetic, public, or explicitly approved data; record procedures and limitations.

Security & governance checkpoint: SEC-02, SEC-03, SEC-04, SEC-07; identify at least one relevant control, potential misuse or failure mode, and proportionate safeguard.

Assessment evidence: Submit a dated lab log or seminar brief with objective, methods, results, references, limitations and reflection.

Reading assignment: Relevant selections from the course reading list, assigned and verified by the instructor before the term.

Week 06 — Uncertainty and probabilistic reasoning

Weekly objectives: (1) Explain the principal concepts, assumptions and limitations of uncertainty and probabilistic reasoning. (2) Apply the week's methods in bayes reasoning worksheet. (3) Recognize and explain risks, limits, privacy and safe use. (4) Interpret evidence and communicate at least one limitation or trade-off.

Instructional plan: Instructor-led conceptual seminar; guided worked example; structured discussion or peer review; independent reading and preparation.

Laboratory / seminar requirements: Bayes reasoning worksheet. A structured worksheet, reproducible example or guided simulation. Use only synthetic, public, or explicitly approved data; record procedures and limitations.

Security & governance checkpoint: SEC-02, SEC-03, SEC-04, SEC-07; identify at least one relevant control, potential misuse or failure mode, and proportionate safeguard.

Assessment evidence: Submit a dated lab log or seminar brief with objective, methods, results, references, limitations and reflection.

Reading assignment: Relevant selections from the course reading list, assigned and verified by the instructor before the term.

Week 07 — Data, features, and supervised learning

Weekly objectives: (1) Explain the principal concepts, assumptions and limitations of data, features, and supervised learning. (2) Apply the week's methods in train/test split lab. (3) Recognize and explain risks, limits, privacy and safe use. (4) Interpret evidence and communicate at least one limitation or trade-off.

Instructional plan: Instructor-led conceptual seminar; guided worked example; structured discussion or peer review; independent reading and preparation.

Laboratory / seminar requirements: Train/test split lab. A structured worksheet, reproducible example or guided simulation. Use only synthetic, public, or explicitly approved data; record procedures and limitations.

Security & governance checkpoint: SEC-02, SEC-03, SEC-04, SEC-07; identify at least one relevant control, potential misuse or failure mode, and proportionate safeguard.

Assessment evidence: Submit a dated lab log or seminar brief with objective, methods, results, references, limitations and reflection.

Reading assignment: Relevant selections from the course reading list, assigned and verified by the instructor before the term.

Week 08 — Classification, regression and evaluation

Weekly objectives: (1) Explain the principal concepts, assumptions and limitations of classification, regression and evaluation. (2) Apply the week's methods in confusion matrix, precision/recall; midterm. (3) Recognize and explain risks, limits, privacy and safe use. (4) Interpret evidence and communicate at least one limitation or trade-off.

Instructional plan: Instructor-led conceptual seminar; guided worked example; structured discussion or peer review; independent reading and preparation.

Laboratory / seminar requirements: Confusion matrix, precision/recall; midterm. A structured worksheet, reproducible example or guided simulation. Use only synthetic, public, or explicitly approved data; record procedures and limitations.

Security & governance checkpoint: SEC-02, SEC-03, SEC-04, SEC-07; identify at least one relevant control, potential misuse or failure mode, and proportionate safeguard.

Assessment evidence: Submit a dated lab log or seminar brief with objective, methods, results, references, limitations and reflection.

Reading assignment: Relevant selections from the course reading list, assigned and verified by the instructor before the term.

Week 09 — Overfitting, generalization and bias

Weekly objectives: (1) Explain the principal concepts, assumptions and limitations of overfitting, generalization and bias. (2) Apply the week's methods in dataset audit. (3) Recognize and explain risks, limits, privacy and safe use. (4) Interpret evidence and communicate at least one limitation or trade-off.

Instructional plan: Instructor-led conceptual seminar; guided worked example; structured discussion or peer review; independent reading and preparation.

Laboratory / seminar requirements: Dataset audit. A structured worksheet, reproducible example or guided simulation. Use only synthetic, public, or explicitly approved data; record procedures and limitations.

Security & governance checkpoint: SEC-02, SEC-03, SEC-04, SEC-07; identify at least one relevant control, potential misuse or failure mode, and proportionate safeguard.

Assessment evidence: Submit a dated lab log or seminar brief with objective, methods, results, references, limitations and reflection.

Reading assignment: Relevant selections from the course reading list, assigned and verified by the instructor before the term.

Week 10 — Neural networks and optimization intuition

Weekly objectives: (1) Explain the principal concepts, assumptions and limitations of neural networks and optimization intuition. (2) Apply the week's methods in interactive network lab. (3) Recognize and explain risks, limits, privacy and safe use. (4) Interpret evidence and communicate at least one limitation or trade-off.

Instructional plan: Instructor-led conceptual seminar; guided worked example; structured discussion or peer review; independent reading and preparation.

Laboratory / seminar requirements: Interactive network lab. A structured worksheet, reproducible example or guided simulation. Use only synthetic, public, or explicitly approved data; record procedures and limitations.

Security & governance checkpoint: SEC-02, SEC-03, SEC-04, SEC-07; identify at least one relevant control, potential misuse or failure mode, and proportionate safeguard.

Assessment evidence: Submit a dated lab log or seminar brief with objective, methods, results, references, limitations and reflection.

Reading assignment: Relevant selections from the course reading list, assigned and verified by the instructor before the term.

Week 11 — Language models and transformer foundations

Weekly objectives: (1) Explain the principal concepts, assumptions and limitations of language models and transformer foundations. (2) Apply the week's methods in tokenization and attention demonstration. (3) Recognize and explain risks, limits, privacy and safe use. (4) Interpret evidence and communicate at least one limitation or trade-off.

Instructional plan: Instructor-led conceptual seminar; guided worked example; structured discussion or peer review; independent reading and preparation.

Laboratory / seminar requirements: Tokenization and attention demonstration. A structured worksheet, reproducible example or guided simulation. Use only synthetic, public, or explicitly approved data; record procedures and limitations.

Security & governance checkpoint: SEC-02, SEC-03, SEC-04, SEC-07; identify at least one relevant control, potential misuse or failure mode, and proportionate safeguard.

Assessment evidence: Submit a dated lab log or seminar brief with objective, methods, results, references, limitations and reflection.

Reading assignment: Relevant selections from the course reading list, assigned and verified by the instructor before the term.

Week 12 — Generative AI, prompting and retrieval

Weekly objectives: (1) Explain the principal concepts, assumptions and limitations of generative ai, prompting and retrieval. (2) Apply the week's methods in grounded vs ungrounded answer comparison. (3) Recognize and explain risks, limits, privacy and safe use. (4) Interpret evidence and communicate at least one limitation or trade-off.

Instructional plan: Instructor-led conceptual seminar; guided worked example; structured discussion or peer review; independent reading and preparation.

Laboratory / seminar requirements: Grounded vs ungrounded answer comparison. A structured worksheet, reproducible example or guided simulation. Use only synthetic, public, or explicitly approved data; record procedures and limitations.

Security & governance checkpoint: SEC-02, SEC-03, SEC-04, SEC-07; identify at least one relevant control, potential misuse or failure mode, and proportionate safeguard.

Assessment evidence: Submit a dated lab log or seminar brief with objective, methods, results, references, limitations and reflection.

Reading assignment: Relevant selections from the course reading list, assigned and verified by the instructor before the term.

Week 13 — Explainability, reliability and safety

Weekly objectives: (1) Explain the principal concepts, assumptions and limitations of explainability, reliability and safety. (2) Apply the week's methods in model card and failure analysis. (3) Recognize and explain risks, limits, privacy and safe use. (4) Interpret evidence and communicate at least one limitation or trade-off.

Instructional plan: Instructor-led conceptual seminar; guided worked example; structured discussion or peer review; independent reading and preparation.

Laboratory / seminar requirements: Model card and failure analysis. A structured worksheet, reproducible example or guided simulation. Use only synthetic, public, or explicitly approved data; record procedures and limitations.

Security & governance checkpoint: SEC-02, SEC-03, SEC-04, SEC-07; identify at least one relevant control, potential misuse or failure mode, and proportionate safeguard.

Assessment evidence: Submit a dated lab log or seminar brief with objective, methods, results, references, limitations and reflection.

Reading assignment: Relevant selections from the course reading list, assigned and verified by the instructor before the term.

Week 14 — AI in society: work, education, creativity and power

Weekly objectives: (1) Explain the principal concepts, assumptions and limitations of ai in society: work, education, creativity and power. (2) Apply the week's methods in structured evidence debate. (3) Recognize and explain risks, limits, privacy and safe use. (4) Interpret evidence and communicate at least one limitation or trade-off.

Instructional plan: Instructor-led conceptual seminar; guided worked example; structured discussion or peer review; independent reading and preparation.

Laboratory / seminar requirements: Structured evidence debate. A structured worksheet, reproducible example or guided simulation. Use only synthetic, public, or explicitly approved data; record procedures and limitations.

Security & governance checkpoint: SEC-02, SEC-03, SEC-04, SEC-07; identify at least one relevant control, potential misuse or failure mode, and proportionate safeguard.

Assessment evidence: Submit a dated lab log or seminar brief with objective, methods, results, references, limitations and reflection.

Reading assignment: Relevant selections from the course reading list, assigned and verified by the instructor before the term.

Week 15 — Synthesis and project presentations

Weekly objectives: (1) Explain the principal concepts, assumptions and limitations of synthesis and project presentations. (2) Apply the week's methods in reproducibility review and oral defense. (3) Recognize and explain risks, limits, privacy and safe use. (4) Interpret evidence and communicate at least one limitation or trade-off.

Instructional plan: Instructor-led conceptual seminar; guided worked example; structured discussion or peer review; independent reading and preparation.

Laboratory / seminar requirements: Reproducibility review and oral defense. A structured worksheet, reproducible example or guided simulation. Use only synthetic, public, or explicitly approved data; record procedures and limitations.

Security & governance checkpoint: SEC-02, SEC-03, SEC-04, SEC-07; identify at least one relevant control, potential misuse or failure mode, and proportionate safeguard.

Assessment evidence: Submit a dated lab log or seminar brief with objective, methods, results, references, limitations and reflection.

Reading assignment: Relevant selections from the course reading list, assigned and verified by the instructor before the term.

Indicative reading list

• Russell & Norvig, Artificial Intelligence: A Modern Approach, selected introductory and search chapters.
• Mitchell, Machine Learning, selected foundational chapters (or an accessible equivalent).
• Turing (1950), “Computing Machinery and Intelligence.”
• NIST AI Risk Management Framework (2023), introductory sections.
• Selected historical primary sources and contemporary peer-reviewed critiques assigned by instructor.

Capstone and evaluation evidence

The final submission must state the problem and constraints, method and baseline, data/source provenance, reproducible procedures, quantitative or qualitative evaluation, limitations, ethical and security implications, and evidence supporting conclusions. Include a short individual oral defense. Instructor will publish detailed rubrics and resource/accessibility alternatives before enrollment.

Quality assurance before academic adoption

Faculty peer review; verification of reading accessibility and rights; learning-outcome-to-assessment mapping; workload and credit-hour calibration; disciplinary prerequisites; inclusive pedagogy review; and alignment with the adopting institution's academic governance process.

Curriculum alignment and instructor delivery

This course is part of the proposed IAIGCRM Secure & Governed AI Development Curriculum. It is designed for potential delivery by IAIGCRM faculty or the Institute's founder, subject to final staffing, scheduling, enrollment, institutional agreements and resource availability. No enrollment, credits or accreditation are currently offered by this draft. The graduate integrative capstone is AI-GR551. Security baseline: SEC-02, SEC-03, SEC-04, SEC-07.

Weekly teaching preparation and laboratory quality gate

For each class, prepare slides, instructor demonstration, learner instructions, environment setup, starter files, a marking rubric, accessibility alternatives, and a verified reading packet. Labs must include a learning objective, prerequisites, data provenance, stepwise tasks, tests, expected outputs, failure cases, evidence submission and cleanup procedure. Students may not attack real systems or use non-consensual personal data.