Communications of the ACM
SWOT Analysis of ChatGPT in Computer Science Education
Introduction
Over the past year, I have published a series of CACM blogs in which I analyzed the introduction of generative AI, in general, and of ChatGPT, in particular, to computer science education (see, ChatGPT in Computer Science Education – January 23, 2023; ChatGPT in Computer Science Education: Freshmen's Conceptions, co-authored with Yael Erez - August 7, 2023; and ChatGPT (and Other Generative AI Applications) as a Disruptive Technology for Computer Science Education: Obsolescence or Reinvention - co-authored with Yael Erez - September 18, 2023).
One of the messages of these blogs was that computer science high school teachers and computer science freshmen clearly see the potential contribution of ChatGPT to computer science teaching and learning processes and highlight the opportunities it opens for computer science education, over the potential threats it poses. Another message was that generative AI, and specifically LLM-based conversational agents (e.g., ChatGPT), may turn out to be disruptive technologies for computer science education and, therefore, should be conceived of as an opportunity for computer science education to stay relevant.
In this blog, we address high school teachers' perspective on the incorporation of ChatGPT into computer science education. In one meeting of a 5-meeting workshop on generative AI in computer science education, high school teachers analyzed the incorporation of generative AI into computer science education through the lens of a SWOT analysis, exploring the strengths (advantages), weaknesses (disadvantages), opportunities and threats of this process. In general, SWOT analysis serves to explore the potential of changing an organizational strategy; in our case, the SWOT analysis methodology helps us explore a new strategy in an educational setting, specifically, the introduction of generative AI into computer science education.
SWOT analysis is commonly presented using a 2X2 table (Figure 1). Due to the rich and wide analysis developed in the workshop by the computer science teachers, their SWOT analysis is presented in a list form, according to the four SWOT sections: strengths, weaknesses, opportunities, and threats. It is interesting to observe that although the teachers' analysis focused on computer science education, it includes many relevant items for education systems in general.
Computer science teachers' SWOT Analysis of the assimilation of generative AI into computer science education
Strengths
- Teaching
- Generative AI supports learning and teaching processes in an inviting environment.
- Generative AI makes it possible to give greater weight to the teaching and learning of skills and problem-solving abilities (such as, critical thinking, self-learning, information search skills, accuracy in defining requirements, creativity, asking questions) over technical details and technical skills.
- Generative AI promotes the integration of new teaching methods and the varying of such.
- Generative AI enriches teaching materials.
- Learning
- Generative AI supports a variety of learning processes and, accordingly, promotes personalized learning.
- Generative AI improves the learning experience through a dialogue between the learners and the generative AI tools.
- Generative AI may increase student involvement and interest in computer science.
- Generative AI enables us to find learning materials for new topics quickly.
- Generative AI may increase the level of abstraction students use in coding tasks since it enables them to describe the needed functionality in a natural language (which is on a higher level of abstraction than the level of abstraction expressed by programming languages).
- Problem solving
- Generative AI enables quick and independent problem-solving processes.
- Generative AI shortens the time needed for a variety of tasks, e.g., research and learning, development and testing.
- Generative AI enriches the collection of proposed solutions and the cases addressed in solving a specific problem.
- In general
- Generative AI is available anywhere and anytime.
- Generative AI is simple to use.
- Generative AI is applied using available tools that are used also for other purposes and usages.
Weaknesses
- Students may rely on the generative AI's answers to do all the work for them and consequently:
- In general, students will stop trying thinking by themselves and will fail to develop problem-solving skills and independent thinking skills;
- Specifically, in the context of computer science education, students will not learn the computer science content, and their knowledge acquisition and learning of algorithmic thinking will be superficial. See Reflective Thought #1 below.
- If the generative AI tool provides incorrect answers that the students do not check, misconceptions will develop.
- Generative AI is incompatible with the education system in (at least) two ways:
- The education system is inable to implement the required changes at the required pace;
- The use of generative AI is not compatible with the Ministry of Education curricula .
- Generative AI may increase gaps between students.
- Teachers who will not implement an appropriate pedagogical approach that is suitable for teaching and learning processes with generative AI may harm learning processes.
- Learning with generative AI depends on the availability of the technology, which may cause technical problems.
- Increased and intensive use of generative AI in teaching and learning processes increases the carbon footprint of AI (see The Carbon Footprint of Artificial Intelligence, CACM, August 2023, Vol. 66 No. 8, Pages 17-19).
Opportunities
- Generative AI can increase diversity in the teaching and learning of computer science both in relation to both teachers and students. See Reflective Thought #2 below.
- As a result of the increasing use of generative AI in a variety of fields, the importance and expression of computer science in technological developments will grow and increase interest in computer science education.
- Generative AI can identify and understand students' learning processes and offer suitable teaching approaches for individual's learner needs (see, personalized learning in the Strengths section); in turn, this personalized learning process enables more students to express and apply their skills.
- Bridging the historical debate of computer science education. See Reflective Thought #3 below.
Threats
- Teachers who will not change their teaching approach and methods, adapting them to the generative AI era, will become obsolete and devoid of a meaningful role in computer science education.
- New innovative and creative ideas created by humans will not be invented at the same rate as before due to the growing reliance on generative AI in the generation of new content (see for example, Are Large Language Models a Threat to Digital Public Goods? Evidence from Activity on Stack Overflow).
- Human thinking and imagination will be confined and limited since all new content will be developed by generative AI.
- Copyrights may be violated and ethical issues may arise due to unfair and unsuitable use of generative AI tools (unless these issues are treated properly prior to comprehensive adoption of generative AI tools, in general, and in educational settings, in particular).
- Data security and students' privacy may be compromised.
Reflective thoughts
Reflective Thought #1: The weakness – Students' relying on AI generated answers. The simple solution for overcoming this weakness is to prohibit the use of generative AI in computer science education. However, instead of applying this immediate and easy-to-implement solution, forfeiting all of AI's advantages listed in the Strengths section above, generative AI should be explored as an opportunity for computer science education to improve learning processes. Accordingly, the use of generative AI in computer science education should be discussed with the students, explaining the consequences of relying on generative AI solutions without either checking the correctness of the solutions or investing the needed time and thought practicing code and other kinds of problem-solving processes. Furthermore, the possibility of using generative AI to improve their learning processes should be demonstrated. At the same time, tasks given to the students should be redesigned in a way that invites the application of high-level cognitive skills.
Reflective Thought #2: The opportunity – Diversity is increased. Generative AI, which enable users to quickly generate new content in the form of texts, images, sounds, animations, 3D models, and other types of data, has the potential to increase diversity in computing, with respect to both teachers and learners. This argument is derived from the style of conversation with the computer that takes place when using generative AI tools and which resembles a conversation with another person, providing teachers and learners a partner to "think" with and be guided by throughout the process. Clearly, such an open environment, that not only allows the expression of ideas in natural language, but also enables to express them in a variety of media, has the potential to increases diversity of computer science educators and learners.
Reflective Thought #3: The opportunity – Bridging the historical debate. During the history of computer science education, a debate has taken place regarding the balance between teaching programming skills and theoretic computer science concepts. Some argued that students should be taught practical, hands-on programming skills to make them job-ready immediately; others believed that a strong theoretical foundation in computer science, including algorithm design, data structures, computability, and formal proving of program correctness, is more important because it allows students to adapt to evolving technologies and solve complex problems, regardless of specific development environments or programming languages. Over the years, the focus has shifted shifted back and forth.
It is proposed that the embracing of generative AI by the community of computer science education may bridge the two approaches: the more progamming-focused aspects of computer science are done using generative AI , while the focus is channeled toward the more theretical aspects of computer science.
Conclusion
This analysis presented in this blog is relevant since computer science educators have started to adopt generative AI, and the pace of its adoption by computer science educators will most probably increase in the near future. Therefore, it is interesting to explore the assimilation process of generative AI in computer science education based on the SWOT analysis presented above. In 1991, Geoffrey A. Moore published the first edition of his book Crossing the Chasm, in which Moore presents a theory that describes the adoption process of innovation. The chasm refers to the stage between the adoption of technology by a small group of early adopters, who are willing to adopt innovations even when they are immature, to the stage in which the innovation is more mature and the innovation is adopted by a larger group of the market (the so-called early majority). Moore's theory analyzes the challenges of crossing this chasm in the process of adopting technology.
With respect to the adoption of generative AI, it seems that the chasm in its adoption process has already been crossed and that, due to the simplicity of using the various generative AI applications available, a huge population, either with or without a technological background, has already adopted them.
Based on the SWOT analysis presented above, the meaningful question for our discussion is: With respect to the community of computer science teachers, what stage of the adoption process of innovation is generative AI at? Has the chasm already been crossed?
Orit Hazzan is a professor at the Technion's Department of Education in Science and Technology. Her research focuses on computer science, software engineering, and data science education. For additional details, see https://orithazzan.net.technion.ac.il/.
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