AVA1000 AR, VR and AI – a practical approach for your business

    • Course code
      AVA1000
    • Number of credits
      7,5
    • Teaching semester
      2024 Autumn
    • Language of instruction
      English
    • Campus
      Online study programme
    • Required prerequisite knowledge

      No special requirements

Course content

The program structure comprises diverse modules starting with fundamental concepts of AI, followed by in-depth exploration of AR and VR technologies, and finally culminating in real-world application and project-based implementations, thereby offering a comprehensive grasp on the content and practicality of AI technologies.

Learning Outcome

A student who has completed the course has the following total learning outcomes, defined by knowledge, skills and general competence:

Knowledge

The student

  • has knowledge of the foundational principles and applications of Artificial Intelligence (AI).
  • has knowledge of the concepts and workings of Extended Reality (XR).
  • has knowledge of different AI algorithms and their application scopes.
  • has knowledge about how AI and XR are influencing a variety of industries and societal norms.
Skills

The student

  • is able to apply AI principles in real-world scenarios using XR tools effectively.
  • is able to proficiently utilize XR technology for AI implementations.
  • is able to familiar the basic AI knowledge used within daily life and business sectors
  • is able to skillfully use AI and XR for problem-solving and innovation.
General competence

The student

  • has understanding, which leads to enhanced critical thinking and problem-solving abilities within the scope of AI and AVR.
  • has understanding that augments adaptability to emerging technologies and encourages a culture of continuous learning.
  • has understanding of the ethical implications and responsibilities associated with AI and AVR usage.
  • has understanding that strengthens collaboration skills through group projects focused on AI and AVR concepts.
  • has understanding that significantly increases their digital literacy in the realm of advanced technology.
Teaching and working methods

This education is delivered online, making use of video lectures, digital learning materials, practical exercises, and self-study techniques. A dedicated online learning platform is employed, beginning the course with an introduction that outlines the theoretical educational approach and manages potential technical concerns.

The course underscores the importance of absorbing theoretical knowledge, whilst maintaining a strong focus on problem-solving skills. It includes individual coursework dedicated to solidifying learning and understanding. Additionally, the learning platform encourages collaboration, discussions, and the sharing of insights among participants.

The course is structured into distinct modules, each zeroing in on a specific domain. Every module commences with an introduction that explains the subject matter and the anticipated learning outcomes. The introduction is followed by a variety of learning resources, which include practical examples, instructional lessons, and interactive tasks. The conclusion of each module is marked by a targeted assignment which serves to reinforce theoretically learnt concepts.

In order to enhance understanding, the learning platform offers topic-focused videos on VR, AR and AI with basic system analysis approach. Paired with a diverse set of exercises, these resources aim to facilitate better grasp and connection of the content and learning materials.

Required coursework
  • Each module is followed by a multiple-choice test which is automatically graded in the learning platform. All the end of module tests must be passed to take the exam.
Assessments
Form of assessmentGrading scaleGroupingDuration of assessmentSupport materialsProportionComment
Home exam
Passed - not passed
Individual
4 Hour(s)
  • All
100
Form of assessment
  • 4 hours individual digital exam

When the assessment period has started there will be a two-week window where the student can hand in the exam.

The exam assignment will be graded with Passed/Not passed.

Faculty
Faculty for Film, TV and Games
Department
Department of Game Development - The Game School
Area of study
Mediefag
Programme of study
AR, VR and AI: Potential and Possibilities
Course level
Half-year programme and shorter (170-LN)