Some Limitations of generative Artificial Intelligence in Solving Logical Problems
DOI:
https://doi.org/10.14712/23362189.2025.4920Keywords:
artificial intelligence, logical problems, Wolf, goat and cabbage, cannibals and missionaries, Chinese room argument, algorithmic approachAbstract
This article discusses the limitations of artificial intelligence in solving classic logical problems, specifically the problems "Wolf, Goat, and Cabbage" and "Three Cannibals and Three Missionaries," including their modification "Four Cannibals and Four Missionaries." The ability of the ChatGPT language model to solve these problems is analyzed, highlighting the difficulties that AI faces in adhering to logical rules and strategies. The article also discusses how the Chinese Room Argument illustrates the limits of algorithmic approaches to problems that require deeper understanding and strategic thinking. In conclusion, it points out that while AI can effectively process and analyze data, some complex logical tasks remain a challenge.
References
Artificial intelligence and the Futures of Learning. (2024). https://dataviz.unesco.org/en
Barassi, V. (2023). Toward a theory of AI errors: Making sense of hallucinations, catastrophic failures, and the fallacy of generative AI. Harvard Data Science Review, (Special Issue 5).
https://doi.org/10.1162/99608f92.ad8ebbd4
Efimova, E. (2018). River crossing problems: Algebraic approach. arXiv: 1802.09369.
Ethical guidelines on the use of artificial intelligence and data in teaching and learning for educators. (2022). European Education Area. https://education.ec.europa.eu/news/ethical-guidelines-on-the-use-of-artificial-intelligence-and-data-in-teaching-and-learning-for-educators
Introducing ChatGPT. (2022). OpenAI. https://openai.com/index/chatgpt/
Kasat, K., Sinha, U., Juneja, S., Ghatge, A., Thorat, N., & Shaikh, N. (2025). Artificial Intelligence in Education: A Comprehensive Examination of Integration, Impact, and Future Implications. In A. Mirzazadeh et al. (Eds.), Optimization and Data Science in Industrial Engineering (pp. 182-198). Springer.
https://doi.org/10.1007/978-3-031-81458-7_11
LISP and the dawn of Artificial Intelligence: A historical and contemporary perspective. (2025). Quantum Zeitgeist. https://quantumzeitgeist.com/lisp-and-the-dawn-of-artificial-intelligence/
Liu, H., Ning, R., Teng, Z., Liu, J., Zhou, Q., & Zhang, Y. (2023). Evaluating the logical reasoning ability of ChatGPT and GPT-4. arXiv: 2304.03439.
Martin, F., & Bolliger, D. U. (2018). Engagement matters: Student perceptions on the importance of engagement strategies in the online learning environment. Online Learning, 22(1).
https://doi.org/10.24059/olj.v22i1.1092
Mason, J., Burton, L., & Stacey, K. (2010). Thinking mathematically (2nd ed.). Pearson.
Minsky, M. (1961). Steps toward artificial intelligence. Proceedings of the IRE, 49(1), 8-30.
https://doi.org/10.1109/JRPROC.1961.287775
Minsky, M. (1987). The society of mind. The Personalist Forum, 3(1), 19-32.
https://doi.org/10.5840/persforum19873112
Mitchell, M. (2019). Artificial intelligence: A guide for thinking humans. Farrar, Straus, and Giroux.
Opesemowo, O. A. G., & Ndlovu, M. (2024). Artificial intelligence in mathematics education: The good, the bad, and the ugly. Journal of Pedagogical Research, 8(3), 333-346.
https://doi.org/10.33902/JPR.202426428
Park, J., Teo, T. W., Teo, A., Chang, J., Huang, J. S., & Koo, S. (2023). Integrating artificial intelligence into science lessons: Teachers' experiences and views. International Journal of STEM Education, 10(1), 61.
https://doi.org/10.1186/s40594-023-00454-3
Pesovski, I., Santos, R., Henriques, R., & Trajkovik, V. (2024). Generative AI for customizable learning experiences. Sustainability, 16(7), 3034.
https://doi.org/10.3390/su16073034
Plevris, V., Papazafeiropoulos, G., & Jiménez Rios, A. (2023). Chatbots put to the test in math and logic problems: A comparison and assessment of ChatGPT-3.5, ChatGPT-4, and Google Bard. AI, 4(4), 949-969.
https://doi.org/10.3390/ai4040048
Pólya, G. (2004). How to solve it: A new aspect of mathematical method. (2nd ed.). Princeton University Press.
Rudolph, J., Tan, S., & Tan, S. (2023). War of the chatbots: Bard, Bing Chat, ChatGPT, Ernie and beyond. The new AI gold rush and its impact on higher education. Journal of Applied Learning & Teaching, 6(1), 364-389.
https://doi.org/10.37074/jalt.2023.6.1.23
Sahito, Z. H., Sahito, F. Z., & Imran, M. (2024). The role of artificial intelligence (AI) in personalized learning: A case study in K-12 education. Global Educational Studies Review, IX(III), 153-163.
https://doi.org/10.31703/gesr.2024(IX-III).15
Searle, J. R. (1980). Minds, brains, and programs. Behavioral and Brain Sciences, 3(3), 417-424.
https://doi.org/10.1017/S0140525X00005756
Valeri, F., Nilsson, P., & Cederqvist, A.-M. (2025). Exploring students' experience of ChatGPT in STEM education. Computers and Education: Artificial Intelligence, 8.
https://doi.org/10.1016/j.caeai.2024.100360
Velké revize RVP v ZV. (2024). Velké revize RVP v ZV. https://revize.rvp.cz/
Vrbová, V., Frolík, D., & Rohlíková, L. (2025). Artificial intelligence as a key element of the personal digital environment of university students. In M. Turčáni (Ed.), 15th International Scientific Conference on Distance Learning in Applied Informatics (pp. 285-294). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-81261-3_22
