People decisions, made with machines
When should managers trust, use, and govern AI to create value without creating harm?
Understand the machine → understand the humans → make it work at work.
HR leaders and managers increasingly shape how organizations adopt and govern artificial intelligence. This course examines how modern AI works, including large language models, retrieval systems, and agents. It explores how these technologies are integrated into real organizational workflows. Topics include AI capabilities and failure modes, human judgment in working with AI, and the implications of AI for job design, teams, decision making, and organizational change.
Through cases, hands-on exercises, and an applied project, students evaluate AI use cases and design AI-enabled workflows for Human Resources, with attention to bias, privacy, compliance, governance, and worker impacts.
Dr. Scott Seidenberger
Human Resource Studies, ILR School
Cornell AI Experimenters Faculty Fellow
I am an ILR ’16 alum and came back to Cornell by way of an Air Force jet, a startup exit, and a Ph.D. in data science. My research is on how people understand, direct, and rely on AI at work, and what that does to judgment, opportunity, and the design of jobs.
My recent work examines how language models narrow the set of career options they present to the people relying on them for guidance, how AI portrays employer brands, and how enterprises measure AI adoption and redesign work around it. I run field studies inside HR organizations through Cornell’s CAHRS network.
By the end of the semester, students will be able to
Explain core AI concepts such as large language models, training, inference, and retrieval-augmented generation at a managerial level.
Diagnose common capability limits like hallucination, bias, and privacy to propose mitigations.
Assess the human factors that determine whether AI deployments succeed.
Analyze organizational implications for work design, labor relations, and HR compliance.
Design and experiment with AI pilot planning that implements risk controls and governance.
Build a cross-functional change plan and communicate ROI, controls, and policy alignment to both executives and worker representatives.
AI-enabled course design
The course meets twice weekly for 75 minutes across 28 sessions, organized into three parts.
Survey course
This is a survey course on rapidly evolving emerging technology, meant to teach fundamental principles and concepts about AI — with a focus on generative AI — as they apply to the field of HR. It is not about teaching prompt engineering or any specific model, tool, system, or commercial application. We cover the impacts of generative AI on about a dozen core HR domains in order to drive further student exploration. Students are encouraged to bring their work experience and skills developed in other courses.
Interactive lectures
Core conceptual content taught by lecture, with class participation in discussions.
Hands-on work with generative AI tools
Direct work with real generative AI systems, including a dedicated agent studio lab.
Business cases
Case studies complement the lectures and serve as lecture reinforcement.
Guest practitioners
Senior HR leaders and industry professionals complement the lecture material and are core to the course.
Guest Speakers
Three parts
A course about deploying AI at work should include piloting AI at work
As part of the Cornell AI Experimenters Faculty Fellows program, this semester will have an AI teaching assistant built specifically for ILRHR 5635. The agent supplements office hours and other course functions. It does not replace the instructor, and it is not the authority on grades or policy.
What you will do
Interact with the agent TA over the semester for course questions, readings, assignment coaching, and FAQ.
Complete short surveys about your experience at a few points in the term. Each counts toward out-of-class participation.
Report failures. Wrong answers, confident nonsense, and dead ends are the most useful data, and diagnosing them is a graded skill.
Data and privacy
Conversation logs and survey responses are collected to improve the agent during the semester and to report on the pilot. Anything reported externally is aggregated and de-identified. No student is named or identifiable, and nothing you say to the agent affects your grade. Do not enter sensitive personal information about yourself or others.
Extra credit for improving the agent
Structured bug reports, evaluation cases the agent gets wrong, prompt or knowledge-base contributions, or a short memo proposing a design change. Full criteria and the submission form are on Canvas. Extra credit is capped, is credited only for substantive contributions, and cannot substitute for missed required work.
Many different sources
All readings are articles, working papers, industry reports, short videos, and instructor-provided materials, distributed through Canvas or Cornell Library, or publicly available at no cost.
AI tool access
Several assignments require working directly with generative AI systems. Students work with the instructor to make sure they have the tool access needed for any assignment core to the course.
Foundations
CS50 for Business for the technology from the ground up, Getting Good at Claude for working with generative AI, plus 3Blue1Brown on large language models and Wolfram on what ChatGPT is doing.
Research & reports
The 2026 AI Index Report from Stanford HAI, The Enterprise AI Playbook from the Stanford Digital Economy Lab, working papers on AI and work, and industry adoption reports.
Cases
Two written cases. First on the HR analytics of an AI workforce transformation. Second on org design in an AI-native firm.
Half the grade is class citizenship
Being a good citizen of this course is your obligation throughout: show up, come prepared, contribute, and keep the conversation going between sessions. Two absences are automatically given; further absences follow University guidelines. Midterm feedback on participation keeps you on course for full points.
The ground rules
Use of AI in this course
AI use is permitted and expected on every out-of-class deliverable. Disclosure is required: each submission carries a short process appendix stating which tools were used, the substantive prompts, and what you accepted, what you rejected, and why. The appendix is graded as part of the work, because trust calibration is a learning outcome and the appendix is where it becomes visible. Undisclosed AI use is an academic integrity violation.
The analog classroom
In lectures, discussions, and guest lectures, laptops and phones are put away and notes are taken by hand. Research shows handwriting outperforms typing for retention and forces the compression that makes ideas stick. Summary notes are generated by the instructor and all lecture materials are posted on Canvas.
The Chatham House rule
Guest speakers discuss actual organizational scenarios and decisions, so sessions run under the Chatham House rule: students may use what they hear but may not attribute it to the speaker or their employer outside the classroom. No recording of any kind. Summary notes are provided by the instructor.
Late work
Late submissions lose a letter grade per 24 hours, up to 72 hours, after which work is not accepted. Speaker briefs are the exception and are not accepted late at all, because their purpose is to reach the speaker before class. Extensions are granted for documented circumstances; ask before the deadline rather than after.
Scope of the course
Disagreement about ideas is expected and welcome; disrespect toward people is not. This course is a broad survey of AI as applied to HR, not an AI ethics and society course, so questions of generative AI ethics outside HR functions are out of scope here.
Common questions
Do I need a technical background?
No. Core AI concepts — large language models, training, inference, retrieval — are taught at a managerial level. The goal is the foundational understanding and critical thinking to know what to learn next as the technology keeps changing.
How much coding is involved?
None. You will work hands-on with real generative AI systems, but this is not a course on prompt engineering or on any specific model, tool, or commercial application. It teaches the fundamental principles that outlast the current tools.
Can I use AI on the assignments?
Yes — AI use is permitted and expected on every out-of-class deliverable, with a disclosure appendix that is graded as part of the work. Undisclosed use, or submitting output you cannot explain or verify, is an academic integrity violation.
Who can enroll?
Enrollment is set for MILR students, with no prerequisites, and the course is offered for letter grades only. Cross-registration questions go to the instructor.
Will guest speaker sessions be recorded?
No. Guest sessions run under the Chatham House rule with no recording of any kind. The instructor provides summary notes as part of the course materials.
What if I have to miss class?
Because life happens, two absences are automatically given. Further absences follow University guidelines. Attendance credit is for presence in the room under the analog classroom conditions, with personal devices put away.