Design of interactive intelligent environments, 7.5 Credits

Contents

This advanced level course is aimed to deepen knowledge and skills in responsible design methodology for interactive artificial intelligence (AI) systems, Human-Computer Interaction (HCI) theory and new technology for interacting and collaborating with intelligent software agents and environments. The course builds upon research and theories developed in the intersection of AI and HCI, with focus on socio-technical systems embedding AI, human-AI collaboration and related ethical and social aspects. Some of the core questions in focus during the course will be

1. How are we going to collaborate with AI technology in the future?
2. How can we deal with control when collaborating with a semi-autonomous system? 
3. What happens with control and autonomy when the system learns and changes behaviour over time?
4. How do we design for embedding in the intelligent systems societal values, moral and ethical considerations, weigh the respective priorities of values held by different stakeholders in different multicultural contexts, explain its reasoning and guarantee transparency? and
5. How do we embed a continuing responsible and participatory design process throughout the use of intelligent and collaborative systems?

The course is divided into the following two parts:
Module 1: Theory and methodology (4,5 ECT)
The theoretical part covers theoretical frameworks and methods for designing and evaluating interactive intelligent systems, and adopts a responsible AI perspective. The socio-technical systems perspective will be applied throughout the course with theories and methods supporting the development of socially intelligent systems. This includes human-centric factors vital when developing AI-based interactive systems, such as the effect of use, mechanisms for human development, motivation, behavior change, autonomy, empowerment, competence, relatedness, value for the individual and society, responsibility, accountability and ethics.

Methodology includes in particular responsible AI design, participatory action research approaches and activity-centered design. The theory and methodologies are applied in group exercises and in an interdisciplinary project in Module 2.
 
Module 2: Practical application of theory and methods in a project (3 ECT)
Theories and methods brought up in Module 1 are applied in a group project, which is conducted in parallel with the theoretical part. The project topic is motivated by a societal challenge and focuses on engaging potential end users and other stakeholders in an interdisciplinary participatory design process. Design activities can be done in labs with technology and material for prototype development and evaluation, and in environments outside of the university that stakeholders provide.

Expected learning outcomes

Knowledge and understanding
After having completed the course the student should be able to:

• Understand, develop and evaluate ethical agency and reasoning abilities as part of the behaviour of interactive, intelligent (proactive, reactive, emergent, social and (semi-)autonomous) systems (FSR1)
• Explain how to accomplish responsible design of interactive, intelligent systems for ensuring transparency, accountability, adherence to social values and different values held by stakeholders (FSR2)
• Understand human-AI interaction as an element of dynamic activity systems based on theories about human activity (including norms and values), and how this translates into computational models useful for human-AI interaction and collaboration (FSR3)

Skills and abilities
After having completed the course the student should be able to:

• Critically analyze applications of theories, methods and tools for developing responsible AI and be able to identify results based on scientifically sound methods (FSR4)
• Develop and evaluate in collaboration with tentative end users, interactive intelligent systems and environments from the personal, social and ethical perspectives, which include aspects of safety, diversity, equal opportunities and responsible AI (FSR5)
• Apply different theories and models relevant for analyzing, designing and constructing interactive intelligent systems in collaboration with tentative end users (FSR6)
• Demonstrate the ability to collaborate in multi-disciplinary teams (FSR7)

Values and attitudes
After having completed the course the student should be able to:

• Demonstrate critical thinking, ethical judgment and understanding of the societal needs when developing interactive, intelligent systems (FSR8)
• evaluate practical results from active involvement of end users and other stakeholders critically and compare these with theoretical, technological and societal expectations (FSR9)

Required Knowledge

Univ: To be admitted you must have (or equivalent) 90 ECTS-credits including 60 ECTS-credits in Computing Science or two years of completed studies within a study programme (120 ECTS-credits). In both cases, includning either the course Interactivity in smart environments (5DV185) or the course Cognitive Interaction Design (5DV190). A Bachelor's degree with a major in Cognitive Science is considered to be equivalent.

Proficiency in English equivalent to Swedish upper Secondary course English A/5. Where the language of instruction is Swedish, applicants must prove proficiency in Swedish to the level required for basic eligibility for higher studies.

Form of instruction

The course consists of lectures, project work in computer labs and other environments, and exercises in small groups. Parts of the teaching require mandatory attendance. In addition to scheduled activities the course also requires individual work with the material.

Examination modes

Examination of Module 1 (FSR 1-3 and 8-9) is done through a written examination in halls. The grade on this part is one of the grades Fail (U), Pass (3) or Pass with Merit (4), and Pass with Distinction (5). For all students who do not pass the regular examination there is another opportunity to do the examination.
 
The examination of Module 2 (FSR 4-9) is done through completing a project in group according to instructions provided during the course. Parts of the project work may be done through field studies outside the university in collaboration with societal organisations, and meetings during the project can be located at such organisation. This module contains examining parts that require mandatory attendance such as field studies, examining project meetings and presentation / demonstration of the project. The grade on the practical part is one of the grades Fail (U) or Pass (G).
 
A student that has failed the practical part of the course but has regularly attended a majority of the project activities can be given a re-exam covering the parts that the student has missed. If a student has not participated in the project activities (or missed a majority of them), the student can enroll in the practical part next time the course is given. The student does not have the right to continue with the same project the next time (s)he attend the course, and may need to start over with the project work in collaboration with a new student group and with a new topic.

On the whole course one of the grades Fail (U), Pass (3) or Pass with Merit (4), and Pass with Distinction (5) is given. At least the grade Pass must be achieved on each part in order to get a grade for the whole course. The grade set for the course is the same as the grade set on module 1.
 
A student who has passed an examination may not be re-examined. A student who has taken two tests for a course or segment of a course, without passing, has the right to have another examiner appointed, unless there exist special reasons (Higher Education Ordinance Chapter 6, section 22). Requests for new examiners are made to the head of the Department of Computing Science.

Examination based on this syllabus is guaranteed for two years after the first registration on the course. This applies even if the course is closed down and this syllabus ceased to be valid.

Deviations from the examination forms mentioned in this syllabus can be made for a student who has a decision on pedagogical support due to disability. Individual adaptation of the examination forms should be considered based on the student's needs. The examination form is adapted within the framework of the expected learning outcomes of the course syllabus. At the request of the student, the course responsible teacher, in consultation with the examiner, must promptly decide on the adapted examination form. The decision must then be communicated to the student.

Transfer of credits
Students have the right to be tried on prior education or equivalent knowledge and skills acquired in the profession can be credited for the same education at Umeå University. Application for credit is submitted to the Student Services / Degree. For more information on credit transfer available at Umeå University's student web, www.student.umu.se, and the Higher Education Ordinance (Chapter 6). A refusal of crediting can be appealed (Higher Education chapter 12) to the University Appeals Board. This applies to the whole as part of the application for credit transfer is rejected.

Other regulations

In a degree, this course may not be included, in whole or in part, simultaneously with another course of similar content. If in doubt, consult the student counselors at the Department of Computer Science and / or program director of programs.