Artificial Intelligence Course Design Planning Framework

Version	Summary	Created by	Modification	Content Size	Created at	Operation
1		Johannes Schleiss	--	2836	2023-12-05 15:46:45	\|
2	Reference format revised.	Lindsay Dong	-3 word(s)	2833	2023-12-06 01:41:42	\|

This entry is adapted from the peer-reviewed paper 10.3390/educsci13090954

The use of Artificial Intelligence (AI) has become key in numerous domains, emphasizing the need for education in this field. The interdisciplinary nature of AI and its relevance across various sectors call for an integration of AI topics into university curricula. This article introduces the "AI Course Design Planning Framework", a comprehensive tool designed to structure the development of domain-specific AI courses at the university level. The AI Course Design Planning Framework forms a visual and practical tool for instructors and course developers in the higher education or professional education context with a special focus on non-computer science (non-CS) students. It can be used as a means to gather ideas, innovate, plan and communicate ideas for domain-specific AI courses. The framework can be used as a self-contained instrument for individuals, in tandem with AI and domain experts or in a workshop setting with multiple people. Scholars suggest filling it from left to right, first considering the questions on AI in the domain, the learning environment of the course and last, the course implementation.

AI education AI teaching AI literacy course development tool course planning

1. Introduction

Artificial intelligence (AI) literacy describes the broad, general knowledge and skills of individuals who interact with AI technology ^[1]. At the same time, the application of AI and the respective required competencies differ across domains and disciplines ^[2]. In this context, education about AI (AI education) goes beyond the concept of AI literacy and describes the education about domain-specific and interdisciplinary AI competencies that take into account the domain requirements and the application of AI in the domain.

While there are already several frameworks and experiences with AI literacy courses for different target groups ^[3]^[4]^[5]^[6]^[7], there is still a lack of domain-specific AI education courses. The interdisciplinary nature of domain-specific AI education poses a set of challenges. First, an understanding of the background, prior experiences and initial competencies of learners is necessary ^[8]^[9]. In the context of domain-specific AI education, students come from different disciplines and with different prior knowledge and skills, which are essential for a deeper understanding of AI ^[6]. This includes, for example, mathematical and statistical competencies, technical understanding and experience in computational thinking. Second, instructors teaching domain-specific AI courses often combine their experience in their domains with the additional cross-disciplinary topic of AI ^[10]^[11]^[12]. Thus, teaching a domain-specific AI course requires a thorough (self-)reflection of the instructors’ competencies in AI and their role in the learning process ^[11]. Third, the complexities of AI are hard to grasp and the development of new AI technology and tools advances at a rapid pace. At the same time, AI technology has different use cases, implications and underlying data in each discipline ^[13]^[14]. Thus, domain-specific AI education needs to bridge the understanding of AI and the understanding of requirements of its application in the domain.

Different types of AI Education

AI education can be distinguished into three areas that target different goals. First, AI literacy education aims at increasing the basic AI knowledge and skills of the general population. In this area, a wide variety of initiatives and experiences already exist that focus on different target groups, such as schoolchildren ^[7]^[15], college students ^[16]^[17], university students ^[5] or the general population ^[4]. AI literacy education can take many forms, from traditional classroom teaching to (open) online courses ^[18]^[19]. Materials for AI literacy education often try to convey a basic understanding of AI concepts and provide accessible explanations about the potential, opportunities and risks of AI. Second, expert AI education focuses the competencies to further develop AI methods and AI research. It is aimed at a deep understanding of the theoretical foundations, modeling techniques, model architectures, current limitations in the field and possible advancements of methods ^[20].

Related Course Planning Frameworks

Several general course planning frameworks and instructional design methodologies already exist. Examples are the ADDIE instructional design approach ^[21], Kern’s six-step approach to curriculum development ^[22], Understanding by Design ^[23], Constructive Alignment ^[24] and the Merrill’s Principles of Instruction ^[25].

The ADDIE approach ^[21] is an established process-based approach to instructional design, consisting of five development stages of analyzing, designing, developing, implementing and evaluating. It is a general, flexible, iterative and integrative learning design approach that helps educators identify necessary learning needs and develop appropriate learning activities to achieve desired learning outcomes.

Similarly, Kern’s six-step approach to curriculum development ^[22] uses a generic, flexible and structured approach to plan and develop curricula and courses. The approach includes six steps, namely (1) problem identification and general needs assessment, (2) targeted needs assessment, (3) goals and objectives, (4) educational strategies, (5) implementation and (6) evaluation and feedback.

Understanding by Design is a course design approach that relies on the idea of backward design, starting with developing the learning outcomes of the course, the assessment and then the learning activities. It is closely connected to the Constructive Alignment approach ^[24], which argues for aligning learning outcomes, assessment methods and learning activities.

Focusing on the implementation of learning activities, Merrill’s Principles of Instruction ^[25] established five instructional design principles for developing courses. According to the principles, learning is promoted when (1) learners engage in solving real-world problems, (2) existing knowledge is activated as a foundation for new knowledge, (3) new knowledge is demonstrated to the learner, (4) new knowledge is applied by the learner and (5) the new knowledge is integrated into the learner’s world.

The idea of using a design tool as a practical and visual framework for lesson planning ^[26]^[27], lesson redesign ^[28] or curriculum development ^[29] has been tested before. The use of design tools is mostly inspired by ideas from Design Thinking ^[30] and the Business Model Canvas introduced by Osterwalder et al. ^[31]. The experiences working with these tools indicate that having a simple, concise visual framework that summarizes core ideas on a single page is valuable. Through their simple and visual structure, these design tools allow for rapid, iterative development and create alignment between stakeholders in the design process ^[26]^[27]^[31].

From the lenses of the ADDIE approach, the AI Course Design Planning Framework can be seen as an additional tool in the analyzing and design stages of the process that aims to get an overview of the course outcomes and structure. Similarly, it can be positioned as a structured approach for the first three steps of Kern’s six-step approach. Following the ideas of Understanding by Design and Constructive Alignment, the AI Course Design Planning Framework aims to support educators to develop their respective learning outcomes in domain-specific AI courses. Most course planning frameworks build on identifying relevant learning outcomes based on the experience and pre-existing knowledge of the learners. Similar to other interdisciplinary course settings, the learning outcomes in domain-specific AI education are also influenced by the respective domain. Thus, when integrating AI education into the disciplines, course developers or instructors need to specify the application areas and implications of AI in the respective domain before looking at learning outcomes.

2. AI Course Design Planning Framework

Figure 1 shows a graphical presentation of the AI Course Design Planning Framework as a concise course development tool (A blank version of the canvas is available for download under https://education4ai.github.io/ai-course-design-planning-framework/ (accessed on 01 December 2023)). The framework consists of three interacting “pillars”, namely “AI in the domain”, “learning environment” and “course implementation”. The first pillar “AI in the domain” focuses on the external context of the application of AI in the domain. The second pillar reflects the learning environment in which the course takes place, such as learners and their interaction with AI, the competencies of the instructor and the available internal support. The third pillar describes the course implementation with learning outcomes, assessment and learning activities, all supported by the findings from the previous two pillars. Thus, the first two pillars have a supportive function. They can be interpreted in terms of Kern’s six-step approach as the “needs assessment” component, which serves as the basis for the pedagogical structure of the course.

Figure 1. The AI Course Design Planning Framework with its three pillars focusing on (1) AI in the domain, (2) the learning environment and (3) the course implementation.

AI in the Domain

Describing the use of AI in the domain is the starting point of any endeavor to create a domain-specific AI course, as it determines what content will be taught in the corresponding courses. The subtopics of the pillar are presented in the following subsections.

Domain

The term domain is used to refer to the discipline in which AI is to be applied. For AI applications in medicine, an exemplary domain could be “radiology” and for AI applications in vehicle technology and development, it could be “mechanical engineering”.

Potential AI Use Cases in the Domain

This subtopic is focused on the effect of AI technology in the domain. It helps to structure the topics that will become relevant for students and learners in the near future. Its goal is to support the identification of current use cases and the prognostic assessment of possible future use cases in which AI could play a role in the domain-specific problem solution.

Data in the Domain

The identified AI use cases are usually based on the most relevant type of data in the respective domain. The respective subtopic is not about which data is easy to obtain or how it can be used but rather about the type of data that is involved. Knowledge of typical data in a given domain enables more targeted use of AI techniques and specification of the data. It makes a big difference for the AI techniques which are to be taught whether the domain mainly works with time-series data, texts, images or other data types. Moreover, it is an important consideration whether the data in the domain is abundant or scarce.

Implications of Using AI in the Domain

Another important factor to consider is the potential implications that could arise when using AI in the respective field ^[32]. This mostly concerns ethical, legal and social implications ^[33]^[34]. For example, using AI to support medical triage decisions has different implications than using it to optimize the energy consumption of a manufacturing plant. Understanding the impact of technology on their domain helps students adhere to societal and ethical standards when using or developing AI technologies in their domain.

Additional Learning Resources

The creation of course material can be supported and guided by existing learning material and from oneself, colleagues or other institutions. In particular, Open Educational Resources (OER) can provide a basis for course development and can be used as preparatory or supplementary materials in course design ^[35]^[36].

Overall, the answers to the questions from this pillar build the foundation on which skills and knowledge are to be taught in the course units.

Learning Environment

In addition to the external aspects of AI in the domain, there are several ways in which the learning environment in which the course takes place can influence the pedagogical implementation of the course. Domain-specific AI courses as interdisciplinary courses place a special demand on learners, instructors and internal support. Thus, it is important to fully understand who the learners are, what skills the instructor possesses and which additional internal support is available.

Learners and Their Interaction with AI

Concerning the learners, three considerations are important for domain-specific AI courses. First, it is important to understand which AI skills and related competencies such as mathematical foundations, computational literacy, data literacy or programming skills the learners have acquired beforehand. Second, it is important to clarify the role of the group of learners regarding their interaction with AI to choose relevant demonstrations of AI-applications and an appropriate level of difficulty. The role can be described in different ways, e.g., using the taxonomy suggested by Faruqe et al. ^[37], which presents four groups whose frequency of contact with AI and AI competency requirements differ from each other. According to the authors, the levels in ascending order are “Consumers, the General Public and Policymakers”, “Co-Workers and Users of AI Products”, “Collaborators and AI Implementers” and “Creators of AI” ^[37]. Third, the existing competencies and the future role are influenced by the curricular integration of the course in an overall program. Moreover, the curricular integration determines if it is a mandatory course and correspondingly the expected number of students in the course. Note, that depending on the interdisciplinarity of the course, the group of learners may be more heterogeneous with students from different fields and with different experiences.

Instructors

Next to learners, instructors play an important role in the learning process ^[38]. Domain-specific AI teaching requires a mix of sufficient AI knowledge, domain expertise and pedagogical skills to teach an interdisciplinary course as well as the motivation and time from an instructor’s perspective. The AI knowledge of faculty and instructors tends to be quite heterogeneous, ranging from no previous AI experience to decades of AI research experience ^[11]. Thus, it is important to understand and assess the instructor’s abilities to teach the course. However, if instructors self-assess their knowledge and skills in AI, attention must be paid to possible cognitive biases or heuristics, leading to an under- or overestimation of actual AI skills. In various other professional contexts, it has been found that people relatively rarely assess their skills correctly and it can be assumed that it is no different when assessing one’s own AI knowledge and skills ^[39]^[40].

Internal Support

Internal support, such as budget, personnel restraints, the maximal duration of courses, available data, software and hardware, can be viewed as resources in a positive sense or as limitations in a negative sense. In the context of AI teaching, two important considerations are the availability of data and the availability of hardware and computing resources. Moreover, instructor support (e.g., through training), institutional barriers concerning interdisciplinary teaching and student support (e.g., through additional resources and infrastructure) play a role in designing an interdisciplinary course ^[9].

Course Implementation

The right pillar represents the core of the framework as it combines the findings from the previous pillars. It aims to create a pedagogical structure that can be interpreted as a short version of the final course implementation. The pillar is structured following the Constructive Alignment approach ^[24], aligning the desired learning outcomes, the assessment of those outcomes and the respective learning activities.

Learning Outcomes

Defining the content and scope of the learning outcomes is an important building block in the context of domain-specific AI teaching that is informed by the considerations of the other pillars and determines the focus of the course. To organize learning outcomes in a structured, consistent and verifiable manner, it is recommended to formulate learning objectives ^[24]. Learning objectives should be specific, measurable, achievable, reasonable and time-bound (i.e., “SMART” objectives; ^[41]) wherever possible. Furthermore, they should focus on specific competence levels following Bloom’s Taxonomy ^[42]. The course learning objectives determine the structure of the course and indicate the time and resources spent on the individual topics. The learning objectives should be shared with the students so that they know which aspects of the course are the most relevant for their professional development.

Assessment

Following the Constructive Alignment approach, it is important to consider in advance through which methods and in which way the fulfillment of the learning objectives will be evaluated ^[24]. Assessment in interdisciplinary courses requires to balance the experiences of different groups of learners as well as the targeted outcome with respect to their interaction with AI. In addition to traditional assessment methods such as exams, tests oral presentations or reports, the applied nature of domain-specific AI teaching can also benefit from project- or problem-based assessments that are connected to real-world applications (see ^[43] as an example). Moreover, research in interdisciplinary education indicates that using assessment through reflection can help students to bridge the disciplinary silos ^[9]. Similar to other fields, using different assessment components can be a beneficial and fair approach to account for the different experiences of students from different disciplines ^[44].

Learning Activities

The last step focuses on the learning activities that lead to the desired learning objectives ^[24]. Thus, the focus is on the pedagogical implementation of the overall course design. In this context the Merrill principles of learning ^[25] should be considered to promote an effective learning experience. Experiences from the few domain-specific AI courses that are already conducted today, indicate that a combination of different teaching methods is often used to address the different aspects of AI ^[45]. The overview of the learning activities builds the basis for more detailed planning of the learning activities throughout the course. These could also include using AI-based learning activities.

Intended Use of the AI Course Design Planning Framework

The AI Course Design Planning Framework forms a visual and practical tool for instructors and course developers in the higher education or professional education context with a special focus on non-computer science (non-CS) students. It can be used as means to gather ideas, innovate, plan and communicate ideas for domain-specific AI courses. The framework can be used as a self-contained instrument for individuals, in tandem with AI and domain experts or in a workshop setting with multiple people. The authors suggest filling it from left to right, first considering the questions on AI in the domain, the learning environment of the course and last the course implementation.

References

Long, D.; Magerko, B. What is AI literacy? Competencies and design considerations. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, Honolulu, HI, USA, 25–30 April 2020; pp. 1–16.
Eaton, E.; Koenig, S.; Schulz, C.; Maurelli, F.; Lee, J.; Eckroth, J.; Crowley, M.; Freedman, R.G.; Cardona-Rivera, R.E.; Machado, T.; et al. Blue sky ideas in artificial intelligence education from the EAAI 2017 new and future AI educator program. AI Matters 2018, 3, 23–31.
Chiu, T.K. A holistic approach to the design of artificial intelligence (AI) education for K-12 schools. TechTrends 2021, 65, 796–807.
Kong, S.C.; Zhang, G. A conceptual framework for designing artificial intelligence literacy programmes for educated citizens. In Proceedings of the 25th Global Chinese Conference on Computers in Education (GCCCE 2021), Centre for Learning, Teaching and Technology, The Education University of Hong Kong, Hong Kong, China, 11–15 November 2021; pp. 11–15.
Laupichler, M.C.; Aster, A.; Schirch, J.; Raupach, T. Artificial intelligence literacy in higher and adult education: A scoping literature review. Comput. Educ. Artif. Intell. 2022, 3, 100101.
Ng, D.T.K.; Lee, M.; Tan, R.J.Y.; Hu, X.; Downie, J.S.; Chu, S.K.W. A review of AI teaching and learning from 2000 to 2020. Educ. Inf. Technol. 2022, 28, 8445–8501.
Wong, G.K.; Ma, X.; Dillenbourg, P.; Huan, J. Broadening artificial intelligence education in K-12: Where to start? ACM Inroads 2020, 11, 20–29.
Lindvig, K.; Ulriksen, L. Different, difficult and local: A review of interdisciplinary teaching activities. Rev. High. Educ. 2019, 43, 697–725.
Van den Beemt, A.; MacLeod, M.; Van der Veen, J.; Van de Ven, A.; Van Baalen, S.; Klaassen, R.; Boon, M. Interdisciplinary engineering education: A review of vision, teaching and support. J. Eng. Educ. 2020, 109, 508–555.
Kim, S.; Jang, Y.; Choi, S.; Kim, W.; Jung, H.; Kim, S.; Kim, H. Analyzing teacher competency with TPACK for K-12 AI education. KI-Künstliche Intell. 2021, 35, 139–151.
Ng, D.T.K.; Leung, J.K.L.; Su, J.; Ng, R.C.W.; Chu, S.K.W. Teachers’ AI digital competencies and twenty-first century skills in the post-pandemic world. Educ. Technol. Res. Dev. 2023, 71, 137–161.
Weinberg, A.; Harding, C. Interdisciplinary teaching and collaboration in higher education: A concept whose time has come. Wash. UJL Pol’y 2004, 14, 15.
Greenhill, A.T.; Edmunds, B.R. A primer of artificial intelligence in medicine. Tech. Innov. Gastrointest. Endosc. 2020, 22, 85–89.
Nti, I.K.; Adekoya, A.F.; Weyori, B.A.; Nyarko-Boateng, O. Applications of artificial intelligence in engineering and manufacturing: A systematic review. J. Intell. Manuf. 2022, 33, 1581–1601.
Lee, I.; Ali, S.; Zhang, H.; DiPaola, D.; Breazeal, C. Developing Middle School Students’ AI Literacy. In Proceedings of the 52nd ACM Technical Symposium on Computer Science Education, Virtual, 13–20 March 2021; pp. 191–197.
Touretzky, D.; Gardner-McCune, C.; Martin, F.; Seehorn, D. Envisioning AI for K-12: What should every child know about AI? In Proceedings of the AAAI Conference on Artificial Intelligence, Honolulu, HI, USA, 27 January–1 January 2019; Volume 33, pp. 9795–9799.
Zhou, X.; Van Brummelen, J.; Lin, P. Designing AI learning experiences for K-12: Emerging works, future opportunities and a design framework. arXiv 2020, arXiv:2009.10228.
Druga, S.; Otero, N.; Ko, A.J. The landscape of teaching resources for ai education. In Proceedings of the 27th ACM Conference on on Innovation and Technology in Computer Science Education, Dublin, Ireland, 8–13 July 2022; Volume 1, pp. 96–102.
Ng, D.T.K.; Leung, J.K.L.; Su, M.J.; Yim, I.H.Y.; Qiao, M.S.; Chu, S.K.W. AI Literacy Education for Nonengineering Undergraduates. In AI Literacy in K-16 Classrooms; Springer: Berlin/Heidelberg, Germany, 2022; pp. 99–116.
Wollowski, M.; Selkowitz, R.; Brown, L.; Goel, A.; Luger, G.; Marshall, J.; Neel, A.; Neller, T.; Norvig, P. A survey of current practice and teaching of AI. In Proceedings of the AAAI Conference on Artificial Intelligence, Phoenix, AZ, USA, 12–17 February 2016; Volume 30.
Branch, R.M. Instructional Design: The ADDIE Approach; Springer Science & Business Media: Berlin/Heidelberg, Germany, 2009; Volume 722.
Kern, D.E.; Thomas, P.A.; Bass, E.B.; Howard, D.M. Curriculum Development for Medical Education: A Six Step Approach; JHU Press: Baltimore, MD, USA, 1998.
Wiggins, G.; Wiggins, G.P.; McTighe, J. Understanding by Design; Ascd: Alexandria, VA, USA, 2005.
Biggs, J. Enhancing teaching through constructive alignment. High. Educ. 1996, 32, 347–364.
Merrill, M.D. First principles of instruction. Educ. Technol. Res. Dev. 2002, 50, 43–59.
Hoang, A.D. Lesson Planning Model Canvas-LPMC. EdArXiv 2020.
Instructional Design Central (2022). The Learning Design Canvas™. Instructional Design Central. Available online: https://www.instructionaldesigncentral.com/the-learning-design-canvas (accessed on 23 August 2023).
Schön, S.; Braun, C.; Hohla, K.; Mütze, A.; Ebner, M. The ReDesign Canvas as a tool for the didactic-methodological redesign of courses and a case study. In Proceedings of the EdMedia+ Innovate Learning, Association for the Advancement of Computing in Education (AACE), New York, NY, USA, 20–23 June 2022; pp. 1048–1055.
Willness, C.; Bruni-Bossio, V. The curriculum innovation canvas: A design thinking framework for the engaged educational entrepreneur. J. High. Educ. Outreach Engagem. 2017, 21, 134–164.
Plattner, H.; Meinel, C.; Weinberg, U. Design Thinking; Springer: Berlin/Heidelberg, Germany, 2009.
Osterwalder, A.; Pigneur, Y.; Oliveira, M.A.Y.; Ferreira, J.J.P. Business Model Generation: A handbook for visionaries, game changers and challengers. Afr. J. Bus. Manag. 2011, 5, 22–30.
Borenstein, J.; Howard, A. Emerging challenges in AI and the need for AI ethics education. AI Ethics 2021, 1, 61–65.
Mason, R.O. Four ethical issues of the information age. In Computer Ethics; Routledge: Oxfordshire, UK, 2017; pp. 41–48.
Coeckelbergh, M. Artificial intelligence, responsibility attribution and a relational justification of explainability. Sci. Eng. Ethics 2020, 26, 2051–2068.
Wiley, D.; Hilton Iii, J.L. Defining OER-enabled pedagogy. Int. Rev. Res. Open Distrib. Learn. 2018, 19.
Schleiss, J.; Hense, J.; Kist, A.; Schlingensiepen, J.; Stober, S. Teaching AI Competencies in Engineering using Projects and Open Educational Resources. In Proceedings of the 50th European Society for Engineering Education (SEFI) Anual Conference, Barcelona, Spain, 19–22 September 2022; pp. 1592–1600.
Faruqe, F.; Watkins, R.; Medsker, L. Competency Model Approach to AI Literacy: Research-based Path from Initial Framework to Model. arXiv 2021, arXiv:2108.05809.
Kansanen, P.; Meri, M. The didactic relation in the teaching-studying-learning process. Didakt. Sci. (-S) Teach. Prof. 1999, 2, 107–116.
Kruger, J.; Dunning, D. Unskilled and unaware of it: How difficulties in recognizing one’s own incompetence lead to inflated self-assessments. J. Personal. Soc. Psychol. 1999, 77, 1121.
Davis, D.A.; Mazmanian, P.E.; Fordis, M.; Van Harrison, R.; Thorpe, K.E.; Perrier, L. Accuracy of physician self-assessment compared with observed measures of competence: A systematic review. JAMA 2006, 296, 1094–1102.
Doran, G.T. There’s a SMART way to write management’s goals and objectives. Manag. Rev. 1981, 70, 35–36.
Krathwohl, D.R. A revision of Bloom’s taxonomy: An overview. Theory Pract. 2002, 41, 212–218.
Yuan, H.; Cao, P. Collaborative assessments in computer science education: A survey. Tsinghua Sci. Technol. 2019, 24, 435–445.
Janssen, C.P.; Nouwen, R.; Overvliet, K.; Adriaans, F.; Stuit, S.; Deoskar, T.; Harvey, B. Multidisciplinary and interdisciplinary teaching in the Utrecht AI program: Why and how? IEEE Pervasive Comput. 2020, 19, 63–68.
Xu, J.J.; Babaian, T. Artificial intelligence in business curriculum: The pedagogy and learning outcomes. Int. J. Manag. Educ. 2021, 19, 100550.

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