=Paper=
{{Paper
|id=Vol-3069/longpaper02
|storemode=property
|title=Teaching AI Ethics to Engineering Students: Reflections on Syllabus Design and Teaching Methods
|pdfUrl=https://ceur-ws.org/Vol-3069/FP_02.pdf
|volume=Vol-3069
|authors=Lauri Tuovinen,Anna Rohunen
}}
==Teaching AI Ethics to Engineering Students: Reflections on Syllabus Design and Teaching Methods==
https://ceur-ws.org/Vol-3069/FP_02.pdf
Proceedings of the Conference on Technology Ethics 2021 - Tethics 2021
Teaching AI Ethics to Engineering Students: Reflections
on Syllabus Design and Teaching Methods
Long paper
Lauri Tuovinen1[0000-0002-7916-0255] and Anna Rohunen[0000-0002-4896-7056]
University of Oulu, Oulu, Finland
1
lauri.tuovinen@oulu.fi
Abstract. The importance of ethics in artificial intelligence is increasing, and this
must be reflected in the contents of computer engineering curricula, since the
researchers and engineers who develop artificial intelligence technologies and
applications play a key part in anticipating and mitigating their harmful effects.
However, there are still many open questions concerning what should be taught
and how. In this paper we suggest an approach to building a syllabus for a course
in ethics of artificial intelligence, make some observations concerning effective
teaching methods and discuss some particular challenges that we have
encountered. These are based on the pilot implementation of a new course that
aimed to give engineering students a comprehensive overview of the ethical and
legislative aspects of artificial intelligence, covering both knowledge of issues
that the students should be aware of and skills that they will need in order to
competently deal with those issues in their work. The course was well received
by the students, but also criticized for its high workload. Substantial difficulties
were experienced in trying to inspire the students to engage in discussions and
debates among themselves, which may limit the effectiveness of the course in
building the students’ ethical argumentation skills unless a satisfactory solution
is found. Several promising ideas for future development of our teaching
practices can be found in the literature.
Keywords: artificial intelligence, ethics education, course syllabi, teaching
practices
1 Introduction
Applications of artificial intelligence (AI) involve many ethical challenges. The
increasing autonomy of AI-based systems to make decisions that may have harmful
consequences to living beings raises questions concerning the safety, transparency and
accountability of such systems. Individuals’ rights to privacy and self-determination
are threatened by the collection of vast amounts of personal data to be analyzed using
AI algorithms for purposes such as surveillance and psychological manipulation. There
are various efforts to regulate the use of AI either currently underway or already
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implemented, but given the rate at which AI technology is advancing, it seems doubtful
that policymaking alone will be enough to curb morally questionable uses of AI
effectively. At the forefront of this progress are the AI researchers and engineers, so as
a complementary approach, we should aim to foster a strong sense of professional ethics
among this community. This, in turn, means that ethics education should be included
in AI engineering curricula at higher education institutions.
It is much less obvious, however, what exactly should be taught and how. For
example, if we consider autonomous cars, there are some things that can be imparted
to the students as facts, such as the safety record of these vehicles so far and the current
legislation governing their development and deployment, but arguably the majority of
interesting issues are subject to ambiguity and debate. These include relevant ethical
principles and their interpretation and prioritization (e.g. how an autonomous vehicle
should weigh the safety of passengers against that of other people), broader societal
implications (e.g. effects of the proliferation of autonomous vehicles on the
transportation sector), and implications of possible game-changing future
developments (e.g. artificial general intelligence). A comprehensive syllabus thus
needs to strike a balance between technical and philosophical topics, as well as between
practical topics that the students can apply immediately and more theoretical ones that
enable them to keep up with future developments.
Finding such a balance is a considerable challenge, especially when the time devoted
to ethics in the curriculum is limited. Furthermore, the non-technical nature of much of
the subject matter must be reflected in teaching and evaluation methods, which may
require both students and teachers to adopt unfamiliar ways of thinking, since courses
designed to build engineering skills typically do not expose them to philosophical
concepts or methods. In this paper we examine the problem of teaching AI ethics to
university-level engineering students based on the pilot implementation of a new course
in the spring term of 2021. The course was offered at the University of Oulu, Finland,
to any interested students as a non-mandatory part of a computer engineering
curriculum, with emphasis on practical knowledge and skills that the students can use
to identify and address ethical issues likely to be relevant to them in the present or the
near future, but also some coverage of more theoretical topics.
In the remainder of the paper, we first present a review of related work in Section 2,
focusing on surveys and reports of existing courses dealing with AI ethics. In Section
3 we give an overview of the objectives, topics and implementation of our own AI
ethics course. In Section 4 we propose a framework for categorizing study topics and
an approach to syllabus design based on the framework. In Section 5 we offer some
reflections on teaching methods and practices, based on what we learned from the pilot
implementation. In Section 6 we look at feedback received from students who took the
course, and in Section 7 we present a discussion and conclusions.
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2 Related work
Fiesler et al. (2020) have reviewed general courses on technology ethics in computer
science education, with a subset of AI-specific courses. In their study, they have
analyzed 115 syllabi with respect to course content and learning outcomes. Of these
courses, 10 were AI-specific, while 55 included some content for the topic AI and
algorithms and 27 for AI and robots. The authors also identified some course topics
closely related to AI, such as privacy and surveillance with 61 courses. Their analysis
results show a high variability across technology ethics courses with respect to their
content, which they deem unsurprising considering the lack of standards, as well as the
disciplinary breadth of the syllabi reviewed. However, they highlight the fact that the
observed variability has potential to enable computing ethics educators to learn from
each other and to finally begin to create norms around the learning outcomes. Despite
the variability, some patterns in the syllabi reveal some critical topics, such as privacy,
algorithms, inequality and justice.
Building on this work and another previous study (Saltz et al., 2019), Garrett et al.
(2020) have compiled a new dataset of established syllabi, specifically focusing on AI
ethics courses. They have analyzed 51 courses with AI ethics content in all, including
31 standalone AI ethics courses and 20 technical AI or ML courses with AI ethics topics
integrated into them. With respect to standalone AI ethics courses, they have identified
the following topics categories: bias, automation and robots, law and policy,
consequences of algorithms, philosophy/morality, privacy, future of AI, history of AI.
When it comes to technical AI and ML courses with AI ethics topics, the most common
topics identified were bias, fairness and privacy.
Garrett et al. (2020) further identified some common teaching practices. For
example, the available reading lists for standalone AI ethics courses showed that the
majority of these courses included news articles as reading assignments; therefore, it
seems that incorporating current events into the course material is a common way to
illustrate consequences of AI usage. In some technical courses, the non-technical
content focused on societal considerations or “technology for social good”. However,
including ethical or social implications in technical courses may be inhibited by the fact
that there is too much material to cover. Based on these two reviews by Fiesler et al.
and Garrett et al., it seems that AI ethics teaching in higher education has often been
organized as standalone courses so far, but in parallel with these, AI ethics topics are
also increasingly being integrated into technical AI courses.
Burton et al. (2018) have developed a course where they have employed science
fiction as a tool to teach AI ethics. Through this approach, they aim to move from an
authority-based view to knowledge (typical of fields with a strong practical component
and an established body of knowledge) to equipping students with skills to cope also
with the unforeseen ethical issues in their future work in technology development and
deployment. During the course, the students analyze science fiction stories and brief
articles using ethical theories as both evaluative and descriptive tools to recognize
problems and consider possible solutions from multiple perspectives. The authors
suggest that their course assignments help develop capacity for attention and critical
thought in a manner that serves the students in their professional lives, enabling them
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to identify potential ethical risks related to a given technology or model, as well as to
articulate their arguments about a given ethical approach and see past incomplete or
specious defenses of potentially unethical projects.
Henderson (2019) has designed and implemented a standalone module on data ethics
and privacy, aiming to raise students’ awareness of current debates in computer science
and to teach how to address these issues in practice. The module includes
interdisciplinary content from ethics, law and computer science, and covers the
following learning outcomes: be able to understand various conceptions of ethics and
privacy; be able to critically analyze research literature at the intersection of computer
science, philosophy and the law; be able to understand the effect of, and the source of,
bias or discrimination in a data-intensive system; understand the need for, and
optionally be able to carry out ethical, social, or privacy assessment of data-intensive
projects. The module was delivered as seminar sessions, with the aim to allow deeper
discussions compared to shorter lectures. The course assessment was based on an essay,
peer instruction with essay-style questions on the weekly topics by the students
themselves, and a data protection impact assessment task.
Wilk (2019) proposes content and teaching strategies for a new standalone course
“Computers, Ethics, Law, and Public Policy” that aims to increase computer science
students’ ethical and legal awareness, as well as to promote critical thinking and skills
needed in decision-making in their future work regarding ethical issues. He presents
ethical, legal and public policy issues relevant to building and using intelligent systems.
These include, for example, ethical and legal problems of algorithmic decision-making,
autonomous systems, social media, fake news, journalism, privacy, and big data. The
author also suggests the specific topics to be taught in the course and proposes teaching
strategies supporting the course objectives; these include, for example, discussing
ethical dilemmas and how to make ethical decisions, seminars with defender and
opponent roles, balancing theory and practice through analyzing case studies based on
ethical theories, and utilization of decision-making methodologies.
Slavkovik (2020) has designed and implemented a standalone course derived from
an existing course, “Research Topics in Artificial Intelligence”, where her goal was to
give an overview of the core issues in AI ethics in such a way that it motivates the
students to pursue further learning in this area. The course also aimed to familiarize the
students with the research topic of machine ethics, as well as some of the research
methods and practices in the AI field. The course topics included machine ethics,
explainable AI, fair-accountable-transparent AI, and responsible AI. Learning
outcomes of the course included, for example, identification of the basic ethical
problems related to AI systems, understanding of the premises of the core moral
theories, ability to appraise the ethical aspects of AI problems, and insight into the
research process in machine ethics. Through assessment of the original course
methodology, the author concluded that there was room for implementing more
learning by teaching, learning by reflection, and learning by example. Scientific articles
were used as learning and discussion material. The students were evaluated through an
oral exam and a group project with intermediary assignments.
Fink (2018) has investigated how to find a balance in covering the theory and
practice as well as the philosophy and ethics in an introductory computer science AI
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course. She presents three examples of assignments that can be utilized to teach
concepts of technical, philosophical and ethical issues related to intelligent agents in
this type of courses. In the ethical assignment, students explore Isaac Asimov’s Three
Laws of Robotics and the ethical implications of AI through the movie “I, Robot”. The
assignment focuses on this movie as its theme revolves around Asimov’s laws and what
can happen when they are applied strictly with logic and not considering standard
human sensitivities. Finally, the students need to address the question of the role of pure
logic vs. emotion in ethical behavior, which the author states is a key theme in the
success or failure of an intelligent agent to truly make decisions that are acceptable from
the human perspective.
Williams et al. (2020) have designed and implemented an experimental ethics-based
curricular module for an undergraduate course, “Robot Ethics”. This module aims to
teach usage of human-subjects research methods to investigate potential ethical
concerns arising in human-robot interaction by engaging students in real experimental
ethics research. The students participate in robot ethics research as experimenters,
through which they simultaneously learn methodological approaches to experimental
robot ethics and use these methods to engage with key theoretical concepts. There were
three interdisciplinary learning objectives in this course: normative influence of
technology with the aim of understanding how technologies may affect human
behaviors due to their perception as moral and social agents; experimental ethics with
the aim of understanding how human-subject experimentation can be used to explore
the ethical implications of technology; and ethical research conduct with the aim of
understanding ethical concerns that may arise in the design and execution of
experimental ethics experiments. The students’ learning was assessed through an
inclass quiz on the experiment’s details related to all three learning objectives.
Furey and Martin (2018) have developed a module on ethical thinking about
autonomous vehicles in an AI course. They suggest incorporating this type of
introductory lesson about ethics into a one-semester AI course. Through a modular
approach, students have an opportunity to connect specific AI topics to the related
ethical implications. In this module, students become familiar with the use of thought
experiment through the trolley problem, as well as learn to understand the complex
nature of ethical dilemmas. Regarding the ethics module, the course assessment is
carried out as follows: the final project paper of the course requires discussion of the
ethical implications of the project idea, and in the final examination there is a question
assessing the students’ understanding of the trolley problem.
Shapiro et al. (2020) introduce a data ethics teaching method, Re-Shape, for data
science education, with the aim to teach the ethical implications of data collection and
use in a computing course. Through the tools and activities of the method, students
collect, process and visualize their movement data. Based on these data, critical
reflection and coordinated classroom activities are carried out about data, data privacy,
and human-centered systems for data science. Building on the idea of cultivating care,
students are engaged with the concept of responsibility to other and confronted with the
idea that they are the “other” within systems that collect and use personal data.
Based on the AI ethics teaching examples described in this section, we have made
the following remarks:
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• The learning outcomes and objectives of the described courses and modules
are relatively similar. They aim to increase students’ awareness of the ethical
implications of AI usage, and to train the students to identify and address
ethical issues related to the development and deployment of AI in practice.
• To achieve the learning outcomes, students are typically taught the key
concepts and theories of ethics in parallel with current ethical issues related to
AI usage. Furthermore, critical thinking as well as skills to discuss and
appraise ethical issues are often promoted through teaching methods that
support learning of this type of skills (such as article or case study analyses,
or class discussions). In some courses, the students are also equipped with
skills to use research methods for exploring the ethical implications of AI or
trained to use practical tools to address the ethical issues.
• In higher education AI ethics teaching, a diverse set of methods and strategies
have been reported. Standalone courses typically utilize assignments that
comprise analysis of articles, stories or movies, discussions or seminars on the
identified ethical issues, as well as consideration of how to solve the studied
issues using suitable tools. AI ethics modules that can be integrated into other
courses, for their part, aim to train students e.g. to use specific methods for
investigating ethical issues, as well as to consider the ethical aspects of a
specific application, project or activity.
• It seems that assessment methods other than traditional written exams can be
employed when teaching AI ethics; this may be an approach that matches well
the reported learning outcomes.
Like the majority of the AI ethics teaching examples presented above, our new
course was carried out as a standalone course. Many of the existing courses and modules
comprise topics and learning outcomes that are relatively similar to our ones,
specifically with respect to the ethical issues of AI and the aim to provide the students
with ethics skills needed in the development and deployment of AI systems in practice.
Teaching methods and strategies seem to vary a great deal among the reviewed courses
and modules, and an extensive set of these has been reported in the literature. Similarly
to what Fiesler et al. (2020) suggested, we see this type of variability as an excellent
opportunity to learn from other AI ethics educators.
Borenstein and Howard (2021) have recently presented some recommendations on
how to design AI ethics teaching with the aim to foster a professional mindset for AI
developers and to seriously engage the students with ethical challenges. We find these
recommendations highly relevant to the selection of suitable teaching methods and
strategies when teaching AI ethics to engineering students. Specifically, the authors
suggest three elements to familiarize students with ethical challenges: 1) teaching the
ethical design of AI algorithms, 2) incorporating fundamental concepts of data science
and the ethics of data acquisition through usage of real-world datasets requiring privacy,
fairness and legal issues to be addressed, and 3) offering “ethics across the curriculum”
through systematic inclusion of AI ethics into the curriculum. Based on these
recommendations, for instance, topical examples of misbehaving AI algorithms could
be elaborated in standalone AI ethics courses, while the application of ethical principles
to system design could be considered in technical AI courses.
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3 Course description
The planning of the course, worth 5 ECTS credit points, began in 2020. The full title of
the course was “AI Ethics, Privacy and Legislation”. Because of the COVID-19
pandemic, the course was designed from the start to be taught remotely using the
Moodle online learning platform, with lectures implemented as Zoom meetings. The
learning outcomes for the course were defined as follows:
• Students will be aware of the ethical and legislative aspects and conditions that
need to be considered in the design and deployment of AI applications;
• They will understand the ethical special characteristics of AI applications,
compared to information technology applications in general;
• They will be able to examine existing and hypothetical AI applications from
an ethical viewpoint and identify potential issues;
• They will be able to weigh the benefits of AI applications against their
drawbacks also in a wider, societal context;
• They will be able to apply ethical principles in AI application design.
The course was lectured over a period of 8 weeks, with each week dedicated to one
of 8 course themes. The themes are explained in Table 1. For each week of the course,
two Zoom sessions were planned: a main lecture of approximately 1.5 hours and a
supplementary session of approximately 45 minutes, the latter featuring a short
presentation or demonstration on a specialized topic followed by discussion.
Exceptions to this plan were week 5, when the supplementary session had to be
cancelled due to scheduling conflicts, and week 8, when the supplementary session took
the form of a tutorial for a design methodology.
The course participants were evaluated based on a series of 8 smaller assignments,
one for each course theme, and a larger final assignment spanning all themes. Passing
all 9 assignments was required in order to pass the course. The lecturers reviewed each
submitted assignment and either accepted it as such or sent it back to the student for
revisions; in either case the student would be given some verbal feedback on their work.
The format of the smaller assignments varied from week to week, but as a typical
example, the student would be instructed to choose an AI application and write a short
essay about it, addressing questions related to the theme of the week.
For the final assignment, the students could pick one of two options. Students who
attended a certain number of Zoom sessions could choose to write a lecture diary, in
which they would discuss the topics covered by the lectures and supplementary sessions
and reflect on what they had learned. As an alternative, students could choose to
complete a final exercise, in which they would come up with an AI application concept
and write a report discussing the ethical aspect of the application, guided by the 8 course
themes. This second option was provided so that students who were unable to attend
the lectures or preferred to work independently for other reasons could complete the
course; to study the course themes, they had access to lecture slides as well as items of
further reading curated by the lecturers. Each student had the opportunity to send in an
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incomplete draft of their final assignment and receive feedback on it to help them in
preparing the final submission.
Table 1. The 8 principal themes of the AI ethics course.
Week Theme Topics covered
Essential concepts in ethics and AI,
1 Introduction to AI ethics perspectives on AI ethics
Debates and controversies involving e.g.
2 Controversial AI applications autonomous weapons and surveillance
Role of data in AI, data ethics, privacy,
3 AI and data data ownership, data philanthropy
Accountability and transparency in
4 AI as decision-maker automated decision-making
Societal changes driven/facilitated by AI,
5 AI and society AI divides, AI literacy, good AI society
Overview of AI regulation, active and
6 AI and legislation proposed legislation relevant to AI
Overview of AI ethics guides, review of
7 AI ethics guides some major institutional guides
Implementing AI ethics in practice by
8 Ethical AI design following a design methodology
4 Syllabus design
Among the challenges of designing a balanced syllabus for an AI ethics course, there
are two in particular that we wish to draw attention to. The first one of these is striking
a balance between topics related to ethical reasoning and argumentation and those
related to understanding AI technology and systems. Covering both areas is essential:
the latter to enhance the students’ awareness of AI applications and their ethical
implications, the former to enhance their ability to analyze these implications and to
make justified decisions regarding ethical issues when encountering them in their work.
We refer to this as the philosophy-technology axis.
The other challenge is the rapid rate of progress in AI research and development,
which raises the question of how we can ensure that at least some of the course content
remains relevant to the students also in the future. For dealing with issues that are
immediately relevant to today’s AI practitioners, the course can offer practical tools
such as ethical design guidelines and methodologies, but the further into the future we
look, the less we know about what the capabilities of AI will be, and therefore the less
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likely it is that any concrete guidelines given today will be useful in dealing with the
ethical implications of those capabilities. To help the students gain some degree of
preparedness, the course should cover technology topics representing visions of the
future of AI, as well as philosophy topics that are more theoretical in nature but also
more stable over time. This we refer to as the practice-theory axis.
If we consider these two axes as perpendicular dimensions, we can visualize them
as shown in Fig. 1. We can thus divide possible study topics in AI ethics into four broad
categories, corresponding to the four quadrants of the figure. Counterclockwise from
the top left, these are as follows:
• Timeless Foundations: established concepts, theories and traditions in ethics;
• Practical Guidance: applied ethics principles and guidelines relevant to AI;
• Here and Now: AI applications of the present and the near future and the
ethical issues associated with them;
• Beyond the Horizon: future potential of AI and the ethical implications of
hypothetical scenarios.
Fig. 1. Categorization of study topics in AI ethics based on two perpendicular axes.
Fig. 2 shows a selection of possible topics to be discussed on an AI ethics course,
placed approximately where they are located on these two axes. The grey ellipse
represents an approximation of the area from where we took the bulk of the subject
matter for our course. Notably, the course syllabus was centered on the major ethical
issues identified in present-day AI applications – safety, bias, accountability,
explainability, privacy – and on the topics in the immediate vicinity of these, which
provide essential context for the current issues and/or means for dealing with them. The
least attention was devoted to the topics at the top of the figure, i.e. the most abstract
and/or futuristic ones such as metaethics and superintelligence.
We argue that this represents a reasonable core syllabus for a practically oriented AI
ethics course, and furthermore that this system for classifying study topics can be used
as a practical framework for designing a syllabus for a course with a wider scope.
Expanding the scope with respect to a given aspect of AI ethics can be thought of as
stretching the grey scoping bubble; the visualization of affinities between topics is
useful in ensuring that the course remains a coherent whole, since it suggests clusters
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of closely related topics that it would make sense to include together (and possibly also
to discuss together, so the framework provides some hints for planning the course
structure as well). The overall shape of the bubble serves as an indicator of the
balancedness of the syllabus, so if the bubble becomes strongly elongated in some
direction, this suggests that reviewing the planned syllabus and possibly including some
additional topics for the sake of balance should be considered.
Fig. 2. Placement of topics on the philosophy-technology and practice-theory axes.
5 Teaching methods
A central challenge that we faced in the planning and execution of the course was
presenting the subject matter to engineering students in such a way that it engages them
to think about AI more philosophically than their other courses require them to. Toward
this end, each week of teaching would follow the same general pattern, where the
lecture would introduce the students to the theme of the week from a mostly theoretical
perspective. The role of the supplementary session would then be to have the students
discuss their thoughts with their peers, and the role of the assignment would be to have
them apply what they had learned.
Real-world examples of AI applications were used in the Zoom sessions to illustrate
theoretical concepts, and in many of the assignments to provide the students with a
context where they can demonstrate their understanding of the subject matter. Perhaps
predictably, when instructed to choose an application to analyze, the students tended to
choose ones that are close to their own everyday lives; for example, services offered by
Facebook or Google were popular choices. The evaluation of the assignments was based
mainly on the students’ ability to build and defend arguments using the concepts and
theories discussed in the Zoom sessions, although some of them also included a fact-
finding element. Furthermore, several assignments invited the students to show original
thought by proposing ideas for addressing ethical issues associated with a given
application or technology.
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Another category of AI applications that engaged the students’ interest were those
involving palpable risk to core values such as health, safety or liberty. This is suggested
by the popularity of such applications (e.g. medical AI systems, autonomous
vehicles/weapons, predictive policing applications) as assignment subjects, and also by
active audience participation during the week 2 lecture on controversial AI applications.
The first recorded case of a pedestrian killed by an autonomous car was used already in
the first lecture as an introductory example, since it provides an effective way to
illustrate a number of key points:
• A malfunction in an AI system may have severe harmful consequences,
including loss of life.
• When this happens, it may be hard to ascertain why it happened and who is
morally or legally responsible.
• Even a hypothetical perfect AI system may encounter a situation where it has
to make a value-based choice between multiple bad options.
• Short-term harm may be inevitable in the pursuit of long-term good, and the
justification of such harm is also debatable.
Overall, the course achieved mixed success at best in inspiring discourse and debate
among the students. This represents a missed opportunity for the students to learn
valuable argumentation skills through peer-to-peer interaction. We can only speculate
on the reasons here; presumably, the teaching methods employed were less than optimal
for this purpose, but on the other hand, it may also be that many of the students (who
were computer engineering majors) were not predisposed to working like this.
Furthermore, it seems plausible that a physical classroom where the participants can
better connect with one another would have been a more suitable environment for this.
This was particularly evident in the final supplementary session, where the participants
were given a tutorial for a design methodology based on a set of cards, designed to be
printed out and manipulated as physical objects. Since this was not an option, the
tutorial was implemented using an online whiteboard application, with the result that
the students participated mainly by adding their own ideas to the whiteboard without
interacting with one another on Zoom while doing it.
Online lecturing did have the unanticipated advantage that the students could use
Zoom chat to ask questions and exchange thoughts even while the lecturer is speaking.
We also set up a message board on Moodle and encouraged the students to use it for
discussions, aiming to engage also those students who feel less comfortable expressing
their views live on Zoom, but this proved unsuccessful. Another benefit of remote
teaching was that this made it convenient to include lectures and presentations by
visiting experts in the course program. Visitors were featured in two of the main lectures
and three of the supplementary sessions; since all of these were planned from the start
to be implemented as Zoom meetings, the visitors could deliver their presentations from
where they are normally based and no special arrangements were required, making
physical distance a non-factor in deciding which external experts to invite as visitors.
Besides contributing their expertise to the course, the visitors enabled the principal
lecturers to focus their efforts on course themes closer to their own respective areas of
expertise, resulting in a higher overall standard of teaching quality than would otherwise
have been achievable.
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6 Student feedback
Feedback on the course was solicited from the participating students using two
anonymous surveys. A standard feedback form, generated automatically for every
course at the university, was filled in and submitted by 7 course participants. The form
included 11 statements to be rated on a Likert scale from 1 (totally disagree) to 5 (totally
agree). The results are summarized in Table 2. The students were also requested to
evaluate their workload during the course, bearing in mind that 1 ECTS credit is
equivalent to 27 hours of student work. The answer options and corresponding integer
values were no workload (2), light workload (1), suitable workload (0), high workload
(1) and very high workload (2); the answers were split between suitable workload and
very high workload for an average of 1.14.
Table 2. 11 statements about the course, rated by students on a Likert scale.
Statement Avg
I achieved the course learning outcomes and course objectives. 3.86
Course content helped me to achieve learning outcomes. 3.57
The course content supported my progression towards expertise in my field. 4.00
Teaching methods supported learning and helped me to achieve learning outcomes. 3.71
Course material supported my learning. 3.57
Instructions to the course tasks were clear. 3.86
There was enough support and guidance in the course. 3.57
Assessment methods and criteria supported my learning. 4.14
There was enough time to complete the tasks in the course. 4.14
I have tried to advance my own learning outside the lectures (e.g. reading the lecture
material, reading some literature connected to the topic or searching more 4.43
information).
I have tried to advance my own learning during the lectures by discussing the topic
with other students, by asking questions from the lecturer, by starting discussion in 3.71
the whole group or questioning the teaching.
In addition to these, the standard form had open-ended questions on good practices,
things to develop and other feedback. Notably, several of the answers to these questions
included complaints about the workload of the course assignments. There were also
individual criticisms concerning the course timetable and the supplementary session
concept. Positive feedback was received on lectures, study materials, weekly
assignments and guest lecturers.
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A supplementary feedback form was designed to capture more detailed information
on the students’ expectations and learning outcomes. This form was available in the
course workspace on Moodle and was filled in and submitted by 7 course participants.
Concerning expectations, the form included the following questions:
• What were your reasons for signing up for the course?
• What were your expectations for the course with respect to your personal and
professional development?
• Would you agree that the course fulfilled your aforementioned expectations?
The first two of these were open-ended questions. Among the answers were some
generic motivations such as finding the subject interesting and needing the credits to
complete one’s studies, but several answers also indicated that the student was
expecting knowledge of AI ethics to be an asset in jobs involving AI. The third question
was to be answered on a Likert scale from 1 (strongly disagree) to 5 (strongly agree);
the answers ranged from 3 to 5 for an average of 4.29. Optionally, the students could
specify their answers to the third question; here one student indicated that they would
have liked to learn more about the fundamentals of ethics, while another remarked that
the exercises required a lot of effort and more writing than the student was used to, but
that they were also good practice. Better visualization of lecture materials was also
requested, and numeric grading instead of pass/fail was suggested as an incentive to put
more effort into the assignments.
Concerning learning outcomes, the form included four Likert-scale questions
corresponding to the four basic categories of study topics identified in Section 4. The
theory here was that a balanced syllabus would contribute to the students’ awareness
of ethical issues in AI – both those that are relevant now and those that are still in the
future – as well as their confidence in their ability to deal with these issues in their work.
The results are summarized in Table 3.
Table 3. 4 statements about course outcomes, rated by students on a Likert scale.
Statement Avg
The course increased my awareness of ethical issues that are relevant to AI
applications today. 4.71
The course increased my confidence in my ability to competently address these
current issues in my work, if and when I encounter them. 4.14
The course increased my awareness of ethical issues that may arise in the future,
given the development potential of AI technology. 4.43
The course increased my confidence in my ability to competently address these
future issues in my work, if and when they arise. 4.29
7 Discussion and conclusions
In this paper we discussed the teaching of AI ethics to engineering students, based on
experience of lecturing a pilot course in the spring semester of 2021. We faced several
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Proceedings of the Conference on Technology Ethics 2021 - Tethics 2021
challenges in designing a comprehensive and balanced syllabus for the course, as well
as in the selection of effective teaching methods. The positive feedback received from
participants suggests that the course was at least a moderate success, but since the
number of students who took the course was small and only a fraction of them answered
the feedback surveys, the results mut be interpreted with caution.
The aspect of the course that received the most criticism from the students was the
workload. The most obvious candidate for an explanation is that the effort required to
complete the assignments was underestimated when planning the course, but there are
other possible contributing factors. For example, if the format of the assignments was
such that it took the students out of their comfort zone – as engineering students they
would be more familiar with e.g. programming assignments than essay writing – then
this may have affected the students’ perception of the course workload. Another
possible factor is that both the individual assignments and the course as a whole were
graded on a simple pass/fail grading scale, so there were no rewards other than words
of praise from the evaluators to be earned by surpassing the minimum requirements
(as one student explicitly pointed out in their feedback). This is perhaps the most
important issue to be addressed when planning future iterations of the course.
As another student noted in their feedback, commenting on the workload, the course
format required a lot of effort from the teachers as well, which is an important point
since it raises a question concerning the scalability of the course. Out of an original 30
students who signed up for the course, roughly half completed all the assignments – the
high dropout rate perhaps another indicator that the students found the workload higher
than expected – which kept the effort required to review the submissions manageable,
but had all 30 completed the course, the allocated teaching resources would have been
stretched to their limits. The issue here is that designing an alternative to the current
assignment format that scales up for a significantly larger number of students while still
effectively building and testing their ethical argumentation skills is far from trivial.
Getting the students engaged in debates among themselves would both develop these
skills and reduce the burden on the teachers, so again the crucial question is how to
effectively facilitate such debates, given that there may be many students who are not
comfortable with this type of work. This is another major aspect of the course that will
need to be addressed in future development.
Among the practices reported in the studies reviewed in Section 2, there are some
that seem particularly useful and promising for the future development of our teaching.
For example, as there is still a lack of established textbooks, we should maintain and
regularly update our current reading list and make sure that it includes both scientific
papers and news articles (the latter to illustrate the real-world consequences of AI use).
Due to the rapid technological development and changes expected in the field, the need
to update the list should be reviewed regularly. As engineering is a typical field with a
strong authority-based view to knowledge, it could be fruitful to think even more about
how to move from this type of thinking to providing our students with skills to deal
with unforeseen ethical issues in their future work tasks. Some of the proposed methods
in the reviewed studies could fulfill this need well and would fit our course
implementation, such as seminars with formal defender and opponent roles or case
study analyses. Finally, we may also consider whether we should extend AI ethics
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teaching to reach a larger portion of the students, as well as how to make the connection
between technical AI topics and their ethical implications clearer; in addition to our
current standalone course, AI ethics modules or assignments could be integrated into
technical AI courses starting from the early stages of engineering study programs.
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