Fall 2026 CSC 586C: Topics in Computer Systems and Software: "Human-AI Collaboration in Responsible SE" (Units: 1.5)

Accessible Learning

The University of Victoria is committed to creating a learning experience that is as accessible as possible. If you are registered with the Centre for Accessible Learning and anticipate or experience any barriers to learning in this course, please feel welcome to discuss your concerns with me. If you are a student with a disability or chronic health condition, you can meet with a CAL advisor to discuss access and accommodations.

How to contact CAL:  https://www.uvic.ca/accessible-learning/students/how-to-register/index.php.

Academic Concessions

The university recognizes its responsibility to offer academic concessions to students whose ability to complete course requirements is interrupted by unexpected and unavoidable circumstances or conflicting responsibilities.

Review the Academic Concession Regulation:

https://www.uvic.ca/calendar/future/undergrad/index.php#/policy/HJjAxiGO4?bc=true&bcCurrent=11%20-%20Academic%20Concessions&bcGroup=Undergraduate%20Academic%20Regulations&bcItemType=policies

and web site:

https://www.uvic.ca/students/academics/academic-concessions-accommodations/request-for-academic-concession/index.php#ipn-undergraduate-requests-for-academic-concession.

Breadth Category

Applications

Course Overview

Please note that the first tutorial for this course is on September 10 (Thur) at 8:30 a.m. and it is mandatory to attend. 

As artificial intelligence reshapes how we work, learn, create, and make decisions, understanding the future of Human–AI collaboration is becoming one of the most important challenges of our time. As increasingly autonomous, agent-based AI systems assume greater decision-making roles, questions of human agency, ethical responsibility, and technology-value alignment have become more critical than ever. This graduate course introduces the profound paradigm shifts underway in technology development, and current trends in Human–AI Collaboration through the perspectives of digital humanism, value-based technology design, relationality, and responsible innovation.

The course welcomes students from across disciplines; technical concepts will be explained when necessary. 

The course consists of two complementary and interconnected components. First, students will critically engage with recent literature, emerging trends and developments in AI-enabled technology innovation, examining how AI is transforming the activities, roles, relationships, and outcomes involved in technology development. Particular attention will be given to the shifting boundaries of human and machine agency and the implications of these changes for value-sensitive and socially responsible innovation. 

Second, students will empirically study and collaboratively engage with a team of undergraduate students implementing real-world AI-enabled technology-for-social-good in a project being conducted concurrently in SENG480B course this term. Through this real-world case study, students will investigate human–AI collaboration in practice, examining the changing role of software engineers in a technology project, AI as a team member, how meaningful human agency and technology-value alignment can be achieved and sustained during both the design process and post-deployment use. Students will also be providing recommendations and mentor the undergraduates in conducting research relevant to their development project. By combining critical scholarship with hands-on research, the course equips students to contribute to the design, development, and governance of AI-enabled technologies that promote human flourishing, social responsibility, and the public good while preserving the essential role of human agency in increasingly autonomous technological systems. 
 

Learning Outcomes

• become familiar with latest and emerging trends in the shifts in the development of AI-based technologies and their impact in society
• critically analyze the role of humans in the technology development process and in technology design that aligns with user, community, and societal values
• understand the risks and harms of tech-value misalignment in society
• research ethical considerations in the development of AI-based technologies in certain application domains (healthcare, education, social sustainability)

• apply concepts and practices of empirical research design in a case study of technology for social good

   

Topics

• ethics in AI-based system technologies

• human-AI collaboration in AI-based system development

• digital humanism

• relationality in stakeholder management 

• AI as a collaborative team member

• human in the loop

• value-based requirements and software engineering

• end-user software engineering

• perspective taking

Class Participation

This is a research-informed experiential learning course where students are asked to learn and critically analyze current research on human-AI collaboration in the context of a case study of a AI-based technology development project. Rather than simply absorbing instructor-prepared material, students conduct their own research by engaging with the reading material as well as the develoment process, practices, and the team in the case study. Active participation and meaningful engagement in this process during lectures and tutorials are therefore central to the course.

Your participation grade is ongoing and begins in Week 1. Note that the first tutorial is on September 10 (Thurs) at 8:30 a.m. and it is mandatory to attend.  In fact, you are expected to attend all lectures and tutorials throughout the term. You will receive a final participation grade at the end of the course. Feedback on your participation will be provided at the course midpoint and at other times upon request.

Class participation will be assessed based on your active and meaningful contribution to course activities, including:

• attending classes and arriving on time;

• completing assigned class activities, reports, and presentations;

• engaging constructively in class discussions and group activities;

• preparing for and contributing to discussions of course readings;

• asking questions, sharing ideas, and responding thoughtfully to the ideas of others;

• making explicit connections between your contributions, course readings, concepts, and your own project experiences; and

Participation is about more than simply being present. Meaningful participation means actively contributing to the learning environment and to the learning of your peers. You are expected to come prepared, engage critically with the material, contribute to discussions and activities, and reflect on what you are learning.

Note: Mere attendance does not, by itself, guarantee any portion of the participation grade.

Delivery Method and Participation to class and tutorials

Attendance and illness: Attendance in person is expected during the scheduled lectures and all tutorials. Given the fast-pace nature of the course, any lecture or lab you miss can lead you to miss important project activity, deliverable, or insight. If you need to be away, please make sure I as well as your team (if you work in a team) are aware in advance, if possible, and make alternate plans to contribute to the team project work. Missing critical assessments will require submission of the University Request for Extension form.

Attendance at the first lecture on September 10 is mandatory. In addition, for this class, the tutorials start in the first week, i.e. on Thursday September 10 and attendance to the Tutorial on September 10 at 8:30 a.m. is mandatory. The course structure, and the way the students are conducting the research case study of an ongoing project will be discussed, as well as preferences for both projects and (possibly) teams will be collected. The class participation is graded at 15%, 

Office hours: will be conducted in person, or on Teams (or similar synchronous remote communication software like Google Meet, Skype, Discord etc). If my door is open outside those hours, feel free to ask me questions in person.

Grading/remark requests: please email me to request a grade review or remark request within 7 calendar days of the marks being distributed.

Assignments

The assignments in this course are direct outcomes of the student engagement with the required readings and the case study of the Human-AI collaboration in the experiential learning project in SENG480B course. For each assignments, the permitted level of use of AI is indicated, according to the scales described in the AI Guidelines Panel. 

Literature review summaries and Oral presentation (15%) (Level A - NO AI)

Empirical research design Report (10%), due Oct 14th. (Level B - AI -Assisted Idea generation and structuring)

Written logs containing reflections on engagement with case study team, due at end of each development sprint in the case study (10%) (Level A - NO AI)

Report on Conducted research. Data collection and analysis procedures, Analysis results (15%) (Level AI- NO AI)

Final Research and course reflection Paper (25%) (Level C - AI-Assisted Editing)

Quizzes

There is one quiz scheduled for Sep 17, worth 5%

Textbooks

There is no textbook for this course. Readings will be taken from primary and gray literature and made available through Brightspace. Students will also be asked to do additional literature search for material relevant to the case study in the course

Exams

There is no final exam. 

The course has a final presentation scheduled during the exam period. 

Grading

5% quiz 1

15% Required Readings. Assessment: presentation in class
 

40% Case study of human-AI collaboration in a real-world AI-based technology development project

- 10% empirical research study design. Assessment of written report
- 15% engagement with the undergraduate team(s). Assessment of written logs plus quality of engagement 
- 15% research data collection and analysis, and analysis results. Assessment of written report

15% Participation in class discussions of required readings and the case study. Assessment: quality of participation 

25% Final presentation and Course reflection paper. Assessment: quality of insights in presentation and paper

Prerequisite Skills and Knowledge

To succeed in this course, you should 

• feel passionate about current trends in technology and their impact in society
• feel passionate about reading through current research on related topics and reflect on analyzing current practice of technology in society
• be comfortable engaging in discussions of the readings and empirical observations of the use of AI in technology design

Illness & Other Personal Circumstances

It is understood that over the course of three months, each student may face temporary impediments that are outside of their control, such as challenges to physical and mental well-being, necessary extra work shifts, moving, concentration of academic deadlines, and family matters. As such, there are three measures in place in this course to proactively support students through those periods:

• Submission buffer periods: All graded assessments in this course will have a buffer period of at least 48-hours between the due date and submission cut-off. You can use this to deal with technical issues at the time of submission or to self-administer extensions. Note, however, that teaching team support is not expected to be available throughout this buffer period.

Because of these lenient, proactive measures, further requests for academic concessions related to personal circumstances (i.e., outside the scope of CAL), will be subject to a high threshold of documentation and advance notice.

AI Guidelines

Posting of Grades

Typically marks for assignments, examinations, and provisional final grades, are made available through a Learning Management System (LMS) like Brightspace, where each student will be able to view only their own grades. Sometimes numerical marks/grades may be posted publicly to the entire class. In that case, full student numbers or names will not be included with the posted information.

Csc Student Groups

The Engineering & Computer Science Students' Society (ECSS) serves all students registered in an Engineering and Computer Science degree program, including Software Engineering (BSEng). For information on ECSS activities, events and services navigate to https://sites.google.com/view/uvic-ecss/.

Course Policies And Guidelines

Late Assignments: No late assignments will be accepted unless prior arrangements have been made with the instructor at least 48 hours before the assignment due date.
Coursework Mark Appeals: Appeals of marks for coursework will only be considered if received within 7 days of the mark being posted.
Attendance: We expect students attend all lectures and labs. It is entirely the students' responsibility to recover any information or announcements presented in lectures from which they were absent.
Electronic devices in labs and lectures: No unauthorized audio or video recording of lectures is permitted.
Electronic devices in midterms and exams: Calculators are only permitted for examinations and tests if explicitly authorized and the type of calculator permitted may be restricted. No other electronic devices (e.g. cell phones, pagers, PDA, etc.) may be used during examinations or tests unless explicitly authorized.
Plagiarism: Cheating, plagiarism and other forms of academic fraud are taken very seriously by both the University and the Department. You should consult the link given below for the UVic policy on academic integrity. Note that the university policy includes the statement that "A largely or fully plagiarized assignment should result in a grade of F for the course."

The Faculty of Engineering and Computer Science Standards for Professional Behaviour are at https://www.uvic.ca/ecs/_assets/docs/student-forms/professional-behaviour.pdf
U.Vic guidelines and policy concerning fraud and academic integrity are at http://web.uvic.ca/calendar/grad/academic-regulations/academic-integrity.html
U. Vic Privacy Policy: If any student has concerns about their private information being stored or accessed outside of Canada, they are required to inform the course instructor about their concerns before the end of second week of classes.