Course Summary Course Objectives Learning Outcomes Course Materials Teaching Methods Weekly Topics Course Schedule Office Hours Assestment ECTS Calculation Course Policies Learning Tips Print Syllabi Download as PNG

ENS309 Ethics in Engineering and Natural Sciences

Syllabus   |  International University of Sarajevo  -  Last Update on Feb 02, 2026

Referencing Curricula

Syllabus Quick Jump

Search and navigate to any syllabus instantly

HOSTED BY

Genetics and Bioengineering

Spring 2025 - 2026 | 6 ECTS Credits | International University of Sarajevo

Academic Year
2025 - 2026
Semester
Spring
Course Code
ENS309
Weekly Hours
3 Teaching + 0 Practice
ECTS
6
Prerequisites
Junior Standing
Teaching Mode Delivery
Face-to-face
Prerequisite For
-
Teaching Mode Delivery Notes
-
Cycle
I Cycle
Prof. Jane Doe

Muhamed Hadžiabdić

Course Lecturer

Position
Full Professor Dr.
Email
mhadziabdic@ius.edu.ba
Phone
033 957
Assistant(s)
-
Assistant E-mail
-

Course Objectives

To provide students with an understanding of the Ethics in the context of engineering. To present students to the algorithms and theories on resolving conflicting situations in terms of ethical behavior. To present students with case studies related to engineering ethical conflicts.

Learning Outcomes

After successful completion of the course, the student will be able to:

1
Identify ethical issues in medicine, health care and life sciences
2
Provide rational justification for ethical decisions
3
Apply the ethical principles of the Universal Declaration on Bioethics and Human Rights
4
Apply critical thinking and analytical skills to solve problems
5
Develop professional competence, effectiveness, and skills, such as an effective communication in both, oral and in written form; professional and ethical responsibility; team work; class attendance

Course Materials

Required Textbook

  • Blundell, Barry G. Ethics in Computing, Science, and Engineering: A Student's Guide to Doing Things Right. Cham: Springer, 2020.
  • P. Boddington, AI Ethics: A Textbook. Singapore: Springer Nature, 2023.
  • Additional Literature
  • Floridi, Luciano. "The Ethics of Artificial Intelligence: principles, challenges, and opportunities." (2023).
  • DiMatteo LA, Poncibò C, Cannarsa M, eds. The Cambridge Handbook of Artificial Intelligence: Global Perspectives on Law and Ethics. Cambridge University Press; 2022.
  • S. Matthew Liao. Ethics of Artificial Intelligence, Oxford University Press; 2020
  • Teaching Methods

    Lectures
    Case studies
    Class discussions.

    Weekly Topics

    This weekly planning is subject to change with advance notice.
    Week Topic Readings / References
    1 Introduction notes
    2 Data Privacy in (Bio)Ethics notes
    3 Ethical considerations in animal studies notes
    4 Professional Ethics notes
    5 GMO ethical concerns notes
    6 Holiday notes
    7 AI Ethics in Engineering and Natural Sciences notes
    8 Midterm
    9 Sustainability and Responsible Design in Engineering notes
    10 TBA notes
    11 TBA notes
    12 Ethical Accountability in Engineering notes
    13 Biotechnology and Bioethics notes
    14 TBA notes
    15 AI and Creativity, Ethics in AI Governance notes

    Course Schedule (All Sections)

    SectionTypeDay 1Venue 1Day 2Venue 2
    ENS309.1 Course Tuesday 14:00 - 16:50 B F1.23 - Amphitheater I - -

    Office Hours & Room

    Course Office hours will be available here soon.

    Assessment Methods and Criteria

    Assessment Components

    40%x1
    Final Exam
    AI: Not Allowed

    Alignment with Learning Outcomes :  1  2  3

    30%x1
    Midterm Exam
    AI: Not Allowed

    Alignment with Learning Outcomes :  1  2  3

    30%x3
    Assignments
    AI: Not Allowed

    Alignment with Learning Outcomes :  1  2  3  4  5

    IUS Grading System

    Grading Scale IUS Grading System IUS Coeff. Letter (B&H) Numerical (B&H)
    0 - 44 F 0 F 5
    45 - 54 E 1
    55 - 64 C 2 E 6
    65 - 69 C+ 2.3 D 7
    70 -74 B- 2.7
    75 - 79 B 3 C 8
    80 - 84 B+ 3.3
    85 - 94 A- 3.7 B 9
    95 - 100 A 4 A 10

    IUS Grading System

    Letter marks that do not affect student's CGPA:
    • "IP" – In progress is assigned for recording unfulfilled student obligations related to graduation project/thesis/dissertation and internship.
    • "S" – Satisfactory is assigned to a student who passed the examinations that are not numerically graded or whose written assignment has been accepted.
    • "U" – Unsatisfactory is assigned to a student who failed to pass the examinations that are not numerically graded.
    • "W" – Withdrawal signifies that student has withdrawn from the relevant course.
    Additional letter mark that affects student's CGPA:

    "N/A" – Not attending, and it is assigned to a student who is suspended from the course or who does not meet the minimal requirement for attendance on lectures or tutorials. The course lecturer must follow the attendance policy and assign "N/A" in each case of a student failing attendance.

    Late Work Policy

    Information about late submission policies will be shared during class and posted in this section. Please check back for official guidelines.

    ECTS Credit Calculation

    📚 Student Workload

    This 6 ECTS credit course corresponds to 150 hours of total student workload, distributed as follows:

    Lecture Hours

    42 hours ⏳ (14 week × 3 h)

    Report

    18 hours ⏳ (3 week × 6 h)

    Home study

    70 hours ⏳ (14 week × 5 h)

    Midterm exam study

    10 hours ⏳ (2 week × 5 h)

    Final exam study

    10 hours ⏳ (2 week × 5 h)

    150 Total Workload Hours

    6 ECTS Credits


    Course Policies

    Academic Integrity

    All work submitted must be your own. Plagiarism, cheating, or any form of academic dishonesty will result in disciplinary action according to university policies. When in doubt about citation practices, consult the instructor.

    Attendance Policy

    Students are expected to adhere to the attendance requirements as outlined in the International University of Sarajevo Study Rules and Regulations. Excessive absences, whether excused or unexcused, may impact academic performance and eligibility for assessment. Mandatory sessions (e.g., labs, workshops) require attendance unless formally exempted. For detailed policies on absences, documentation, and penalties, please refer to the official university regulations.

    Technology & AI Policy

    Laptops/tablets may be used for note-taking only during lectures. Phones should be silenced and put away during all class sessions. Audio/video recording requires prior permission from the instructor.

    Artificial Intelligence (AI) Usage: The use of AI tools (e.g., ChatGPT, Copilot, Gemini) varies by assessment component. Please refer to the AI usage indicator next to each assessment item in the Assessment Methods and Criteria section above. Submitting AI-generated content as your own work, where AI is not explicitly allowed, constitutes an academic integrity violation.

    Communication Policy

    All course-related communication should occur through official university channels (institutional email or SIS). Emails should include [ENS309] in the subject line.

    Academic Quality Assurance Policy

    Course Academic Quality Assurance is achieved through Semester Student Survey. At the end of each academic year, the institution of higher education is obliged to evaluate work of the academic staff, or the success of realization of the curricula.

    More info

    Article 112: Evaluation of Work of the Academic Staff

    1. At the end of each academic year, the institution of higher education is obliged to evaluate work of the academic staff, or the success of realization of the curricula.
    2. Evaluation of work of each academic staff member is to be carried out in accordance with the Statute of the institution of higher education by the institution as well as by students.
    3. The institutions of higher education are obliged to carry out a students’ evaluation survey on the academic staff performance after the end of each semester, or after the completed teaching cycle for the subject taught.
    4. Evaluation must evaluate: lecture quality, student-academic staff interaction, correctness of communication, teacher’s attitudes towards students attending the teaching activities and at assessments, availability of suggested reading material, attendance and punctuality of the teacher, along with other criteria which are defined in the Statute.
    5. The institution of higher education by a specific act determines the procedure for evaluation of the academic staff performance, the content of survey forms, the manner of conducting the evaluation, grading criteria for the evaluation, as well as adequate measures for the academic staff who received negative evaluation for two consecutive years.
    6. The evaluation of the academic staff performance is an integral process of establishment the quality assurance system, or self-control and internal quality assurance.
    7. Results of the evaluation of the academic staff performance are to be adequately analyzed by the institution of higher education, and the decision of the head of the organizational unit about the employee’s work performance is an integral part of the personal file of each member of academic staff.

    Learning Tips

    Engage Actively

    Be prepared to contribute thoughtfully during class discussions, labs, or collaborative work. Active participation deepens understanding and encourages critical thinking.

    Read and Review Purposefully

    Complete assigned readings or prep materials before class. Take notes, highlight key ideas, and jot down questions. Aim to grasp core concepts and their applications—not just facts.

    Think Critically in Assignments

    Use course frameworks or methodologies to analyze problems, case studies, or projects. Begin early to allow time for reflection and refinement. Seek feedback to improve your work.

    Ask Questions Early

    Don’t hesitate to reach out when something is unclear. Use office hours, discussion boards, or peer networks to clarify concepts and stay on track.

    Course Academic Quality Assurance: Semester Student Survey

    Syllabus Last Updated on Feb 02, 2026 | International University of Sarajevo

    Print Syllabus  

     

     

    Referencing Curricula Print this page

    Course Code Course Title Weekly Hours* ECTS Weekly Class Schedule
    T P
    ENS309 Ethics in Engineering and Natural Sciences 3 0 6
    Prerequisite Junior Standing It is a prerequisite to -
    Lecturer Muhamed Hadžiabdić Office Hours / Room / Phone
    Tuesday:
    13:00-15:30
    Thursday:
    10:00-12:00
    A F1.31 - 033 957 212
    E-mail mhadziabdic@ius.edu.ba
    Assistant Assistant E-mail
    Course Objectives To provide students with an understanding of the Ethics in the context of engineering.
    To present students to the algorithms and theories on resolving conflicting situations in terms of ethical behavior.
    To present students with case studies related to engineering ethical conflicts.
    Textbook Blundell, Barry G. Ethics in Computing, Science, and Engineering: A Student's Guide to Doing Things Right. Cham: Springer, 2020. P. Boddington, AI Ethics: A Textbook. Singapore: Springer Nature, 2023.
    Additional Literature
    • Floridi, Luciano. "The Ethics of Artificial Intelligence: principles, challenges, and opportunities." (2023).
    • DiMatteo LA, Poncibò C, Cannarsa M, eds. The Cambridge Handbook of Artificial Intelligence: Global Perspectives on Law and Ethics. Cambridge University Press; 2022.
    • S. Matthew Liao. Ethics of Artificial Intelligence, Oxford University Press; 2020
    Learning Outcomes After successful  completion of the course, the student will be able to:
    1. Identify ethical issues in medicine, health care and life sciences
    2. Provide rational justification for ethical decisions
    3. Apply the ethical principles of the Universal Declaration on Bioethics and Human Rights
    4. Apply critical thinking and analytical skills to solve problems
    5. Develop professional competence, effectiveness, and skills, such as an effective communication in both, oral and in written form; professional and ethical responsibility; team work; class attendance
    Teaching Methods Lectures, Case studies, Class discussions.
    Teaching Method Delivery Face-to-face Teaching Method Delivery Notes
    WEEK TOPIC REFERENCE
    Week 1 Introduction notes
    Week 2 Data Privacy in (Bio)Ethics notes
    Week 3 Ethical considerations in animal studies notes
    Week 4 Professional Ethics notes
    Week 5 GMO ethical concerns notes
    Week 6 Holiday notes
    Week 7 AI Ethics in Engineering and Natural Sciences notes
    Week 8 Midterm
    Week 9 Sustainability and Responsible Design in Engineering notes
    Week 10 TBA notes
    Week 11 TBA notes
    Week 12 Ethical Accountability in Engineering notes
    Week 13 Biotechnology and Bioethics notes
    Week 14 TBA notes
    Week 15 AI and Creativity, Ethics in AI Governance notes
    Assessment Methods and Criteria Evaluation Tool Quantity Weight Alignment with LOs AI Usage
    Final Exam 1 40 1,2,3 Not Allowed
    Semester Evaluation Components
    Midterm Exam 1 30 1,2,3 Not Allowed
    Assignments 3 30 1,2,3,4,5 Not Allowed
    ***     ECTS Credit Calculation     ***
     Activity Hours Weeks Student Workload Hours Activity Hours Weeks Student Workload Hours
    Lecture Hours 3 14 42 Report 6 3 18
    Home study 5 14 70 Midterm exam study 5 2 10
    Final exam study 5 2 10
            Total Workload Hours = 150
    *T= Teaching, P= Practice ECTS Credit = 6
    Course Academic Quality Assurance: Semester Student Survey Last Update Date: 25/02/2026

    Print this page