Program Description

Schedule note: Upcoming scheduled events are subject to change. A finalized version of this summer's activities will be shared in the archive upon completion.

This 6-week hybrid institute supports computer science and cybersecurity faculty in developing, refining, and implementing instructional materials that integrate artificial intelligence and machine learning into cybersecurity curricula. The program is structured in three phases: an initial in-person immersion (Phase 1), a guided remote refinement period (Phase 2), and a final in-person peer teaching practicum (Phase 3).

Participants begin by building foundational understanding of AI/ML concepts through hands-on labs, then apply these concepts to cybersecurity contexts including intrusion detection, adversarial machine learning, AI system security, responsible use, and privacy. Participants then use AI-assisted prototyping, structured peer review, and teaching rehearsal to design, test, and deliver a complete teaching module (lecture + lab/activity), which is refined through iterative feedback.

The program emphasizes practical, classroom-ready outputs, enabling participants to return to their institutions with materials they can immediately deploy, along with a clear plan for continued implementation and improvement

Expected Outcomes

• Develop and teach a complete AI+cybersecurity module (lecture + lab/activity)
• Apply core AI/ML concepts to cybersecurity problems and contexts
• Design hands-on labs and assignments integrating AI into security courses
• Evaluate and address security risks in AI systems
• Adapt instructional materials to different course levels and institutional contexts
• Assess student learning with aligned objectives, activities, and evaluation strategies
• Present instructional design through peer-reviewed teaching demonstrations

Schedule

Phase 1: Foundations and Applications (in-person)

Week 1

Week 1 at-a-glance:

Time	Monday 6/1	Tuesday 6/2	Wednesday 6/3	Thursday 6/4	Friday 6/5
9:30 - 10 AM	Arrival and Breakfast [SEH Green Wall Atrium]
10 AM - 11 AM	Welcome and Orientation [SEH Lehman]	Supervised Learning Foundations Rebecca [SEH 4040]	Neural Networks Shi [SEH 4040]	Toward agents Shi [SEH 4040]	Guest speaker: Mohit Iyyer [SEH Lehman]
11 AM - 11:30 AM	World View Shi [SEH Lehman]
11:30 AM - 12 PM		Lunch on your own			Provided Lunch with the guest [SEH Green Wall Atrium]
12 PM - 1 PM	Lunch on your own
1 PM - 1:30 PM		Supervised Learning Foundations Continued Rebecca [SEH 4040]	Generative Models and Transformers Shi [SEH 4040]	Robustness and Visualization and Adversaries Robert [SEH 4040]	Group Discussions over Ice-Cream + Claude Code Demo + Transformer Lab Adam, Arkady, Rebecca, Robert, Shi [SEH B1270]
1:30 PM - 2:30 PM	ML Tech Stack Lecture ML Tech Stack Lab Robert [SEH 4040]
2:30 PM - 4 PM		Guided Lab: Supervised Learning Rebecca [SEH 4040]	Guided Lab: LM + Classification Shi [SEH 4040]	Visual Adversarial Attacks Lab Robert [SEH 4040]
4 PM - 5 PM	Free lab time
5 PM - 7 PM	Welcome Reception [SEH Green Wall Atrium]

Day-by-day details:

Date	Topic	Description	Activity
Mon 6/1/26	Orientation + World view and State of AI	Program kickoff, participant introductions, an overview on the state of AI, and an introduction to technicla frameworks.	ML Tech Stack Orientation
Tue 6/2/26	Supervised Learning Foundations	Supervised learning pipeline, evaluation metrics, and a guided classifier lab.	Supervised Learning Lab
Wed 6/3/26	Neural Networks + LLM Foundations	Neural networks, transformers, prompt engineering, and a character-based LLM lab.	LLM Foundations Lab
Thu 6/4/26	Toward Agents + Robustness, Visualization, and Adversaries	LLMs as agents, model robustness against adversarial attacks, and a hands-on visual adversarial attacks lab.	Visual Adversarial Attacks
Fri 6/5/26	Guest speaker: Dr. Mohit Iyyer (UMD)	We are pleased to have Dr. Iyyer present a guest lecture in the morning, “Detecting and characterizing AI use in collaborative writings”; in the afternoon, we will have group discussions over ice-cream, a Claude Code demo, and a transformer lab.	Group Discussions over Ice-Cream + Claude Code Demo + Transformer Lab

Week 2

Week 2 at-a-glance:

Time	Monday 6/8	Tuesday 6/9	Wednesday 6/10	Thursday 6/11	Friday 6/12
10 AM - 11 AM	Applying ML to Cyber Problems Adam [SEH 4040] [slides]	Securing AI Systems Adam [SEH 4040] [slides]	Guest Speaker: David Broniatowski [SEH 4040]	Privacy Arkady [SEH 4040]	Build-it Break-it Fix-it Adam [SEH 4040]
11 AM - 12 PM			Discussion [SEH 4040]
12 PM - 1 PM	Lunch on your own	Lunch on your own	Provided Lunch	Lunch on your own	Provided working lunch
1 PM - 2 PM	Guided Lab: Security Classifier Adam [SEH 4040]	Guided Lab: AI Systems Security Adam [SEH 4040]	Guest Speaker: Elissa Redmiles [SEH 4040]	Human Factors Privacy Shiza [SEH 4040]	Build-it Break-it Fix-it Adam [SEH 4040]
2 PM - 3 PM			Discussion [SEH 4040]
Beyond 3 PM	Free Lab time

Day-by-day details:

Date	Topic	Description	Activity
Mon 6/8/26	Applying ML to Cybersecurity Problems	Map the ML pipeline to intrusion detection, phishing detection, malware classification, and security operations, then translate these patterns into teachable classroom labs.	Security Classifier Lab Design Studio
Tue 6/9/26	Securing AI Systems (slides)	AI threat models, deployment vulnerabilities, prompt injection, leakage, and jailbreaks, with emphasis on system design and basic defenses.	AI Systems Security Lab
Mon 6/8/26	Applying ML to Cybersecurity Problems (slides)	Map the ML pipeline to intrusion detection, phishing detection, malware classification, and security operations, then translate these patterns into teachable classroom labs.	Security Classifier Lab Design Studio
Tue 6/9/26	Securing AI Systems	AI threat models, deployment vulnerabilities, prompt injection, leakage, and jailbreaks, with emphasis on system design and basic defenses.	AI Systems Security Lab
Wed 6/10/26	Morning: Guest Speaker: AI Governance Afternoon: Guest Speaker: AI Abuse	Morning: David Broniatowski (GW) — Beyond the Metrics: Navigating Systems Risk and Trustworthiness under the NIST AI RMF, on the limits of a metrics-only approach to AI governance. Afternoon: Elissa Redmiles (Georgetown) — An AI-Involved Landscape of Sexual Abuse, on threat models, technical defenses, and open questions for AI-powered image-based sexual abuse.	—
Thu 6/11/26	Morning: Privacy in AI/ML and Cybersecurity Afternoon: Human Factors in LLM and AI Privacy	Morning: privacy risks in training data, logs, prompts, and outputs, including data leakage, sensitive cybersecurity datasets, and classroom-safe data practices. Afternoon: Human Factors in LLM and AI Privacy, a presentation and hands-on studio on what users disclose to LLMs, what they consider sensitive, mental-model failures, and contextual integrity.	—
Fri 6/12/26	Build-it / Break-it / Fix-it	All-day synthesis in which teams build an AI-enabled security artifact or teaching lab, exchange it for critique or attack, then fix and document improvements.	Build-it / Break-it / Fix-it Lab

Week 3

Week 3 at-a-glance:

Time	Monday 6/15	Tuesday 6/16	Wednesday 6/17	Thursday 6/18	Friday 6/19
10 AM - 11 AM	Throughline Review and Dataset Realism Rebecca [SEH 4040]	AI Assisted Prototyping and Teaching Methods Adam [SEH 4040]	Guest Speaker: Malihe Aliktani [SEH Lehman]	Cohort Showcase Adam, Arkady, Rebecca, Robert, Shi [SEH 4040]	Juneteenth (Holiday)
11 AM - 12 PM
12 PM - 1 PM	Lunch on your own	Lunch on your own	Provided Lunch with the Speaker	Provided Lunch
1 PM - 3 PM	Lab: Initial Prototype Rebecca [SEH 4040]	Class factory continued Adam [SEH 4040]	Cohort Field Trip	Transition Plan Adam, Arkady, Rebecca, Robert, Shi [SEH 4040]
Beyond 3 PM	Free Lab Time

Day-by-day details:

Date	Topic	Description	Activity
Mon 6/15/26	Throughline Review, Datasets, and Realism	Reconnect Weeks 1-2 concepts, examine dataset realism and classroom simplification, and draft an initial module prototype for peer review.	Initial Prototype and Peer Review
Tue 6/16/26	AI-Assisted Prototyping and Teaching Methods	Use AI tools to draft lectures, labs, rubrics, instructor notes, and student materials while evaluating outputs for correctness, feasibility, privacy, and security realism.	AI-Assisted Teaching Materials Sprint
Wed 6/17/26	Guest Speaker: Malihe Aliktani	Guest Speaker session with Malihe Aliktani in the morning, followed by a Cohort Field Trip in the afternoon.	Cohort Field Trip
Thu 6/18/26	All-Day Showcase and Phase 2/3 Transition	All-day showcase and transition block with short teaching previews, structured critique, a Phase 2 build backlog, and a Phase 3 teaching-demo plan.	Showcase and Transition Planning

Phase 2: Week 4-5: Curriculum Refinement (remote)

Week	Topic	Description	Activity
4	Module build-out and adaptation	Convert the draft work into a cohesive teaching module and adapt it to the home institution context.	Module Build-Out and Adaptation Workshop
5	Testing, revision, and presentation prep	Run a dry run, gather feedback, revise the module, and prepare presentation materials for the final phase.	Testing, Revision, and Presentation Prep Workshop

Phase 3: Week 6: Peer Teaching Practicum (in-person)

Week	Topic	Description	Activity
6	Peer teaching practicum	Deliver the module in a classroom-like setting, receive structured critique, and finalize the classroom-ready version.	Peer Teaching Practicum Prep