AI Poses New Challenges in Energy Education: On the Example of Robotics
V. Vodovozov, Z. Raud 1.Department of Electrical Power Engineering and Mechatronics, Tallinn University of Technology, Tallinn (Estonia) 2026-06-27

Abstract

The arrival of artificial intelligence (AI) rapidlytransforms technological, economic and social life, leading to a rethinking of the role of an engineer and changing requirements for engineering education. This paper presents some results obtained at Tallinn University of Technology in the field of energy education using AI. The research is based on explicit data collection from staff experiences, student communications and feedback, as well as descriptive statistics of learning management systems while the participants studied energy features of robots in two disciplines, namely “Robotics” at the Bachelor's level and “Advanced Robotics” at the Master's level. Students united in a single cohort differed from one another in terms of academic levels, professional prerequisites (mechanical, electrical, and information technology), knowledge backgrounds, and forms and duration of training. At the moment, such distinctions meet the needs of the modern community, in which multidisciplinary professional teams jointly solve complex industrial and societal tasks. At that, robotics disciplines have become a crossroads where three types of AI converge: narrow AI responsible for the robots’ “thinking”, conversational AI interfaces such as ChatGPT, Copilot, or Gemini used to answer numerous educational questions, and generative AI capable of making design and control decisions. Several stages of AI implementation are considered that have yielded the most significant outcomes in students' knowledge and skills acquisition.

Key words:artificial intelligence, energy education, robotics, active learning.

References

[1] C. Müller, World Robotics 2023 – Industrial Robots, IFR Statistical Department, VDMA Services GmbH, Frankfurt am Main, Germany (2023). Available: https://ifr.org/img/worldrobotics/Executive_Summary_WR_Industrial_Robots_2023.pdf.

[2] R. A. Kampa, What is a Serial Robot? (2024). Available: https://www.easytechjunkie.com/what-is-a-serial-robot.htm.

[3] Web Parallel Robots: The High-Speed Robotics Platform (2021). Available: https://howtorobot.com/expert-insight/parallel-robots.

[4] Robotics and Smart Autonomous Systems Technology and Applications (2025). Priyadarshini, R., Mehra, R.M., Sehgal, A. and Singh, P.J. (Eds.), CRC Press, 289 p. Available: https://doi.org/10.1201/9781003537083.

[5] Artificial Intelligence and the Future of Teaching and Learning (2023). Cardona, M.A. (Ed.), Washington DC, 71 p. Available: https://www.ed.gov/sites/ed/files/documents/ai-report/ai-report.pdf.

[6] Artificial Intelligence Based Smart and Secured Applications. Third International Conference proceedings. Revised Selected Papers, Part IV (2024). Rajagopal, S., Popat, K., Meva, D., Bajeja S. and Mudholkar, P. (Eds.), ASCIS Rajkot, India, , Springer, 471 p. Available: https://doi.org/10.1007/978-3-031-86299-1.

[7] Intelligent Technology for Educational Applications: Proceedings of the First International Conference (2024), Lee, K.W. and Wong, L.L. (Eds). ITEA, Kuala Lumpur, Malaysia, Springer, 160 p. Available: https://doi.org/10.1007/978-981-96-4090-4.

[8] B. Han, S. Nawaz, G. Buchanan and D. McKay, “Students’ perceptions: Exploring the interplay of ethical and pedagogical impacts for adopting AI in higher education,” International Journal of Artificial Intelligence in Education (2025), Springer. pp. 1–26. Available: https://doi.org/10.1007/s40593-024-00456-4.

[9] N. K. Viet, “The use of generative AI tools in higher education: Ethical and pedagogical principles,” Journal of Academic Ethics (2025), 23:1435–1455. Available: https://doi.org/10.1007/s10805-025-09607-1.

[10] M. Sposato, “Artificial intelligence in educational leadership: A comprehensive taxonomy and future directions,” International Journal of Educational Technology in Higher Education (2025), Springer, 18 p. Available: https://doi.org/10.1186/s41239-025-00517-1.

[11] A. K. Singh, M. K. Kiriti, H. Singh and A. Shrivastava, “Education AI: Exploring the impact of artificial intelligence on education in the digital age,” Int J Syst Assur Eng Manag (2025), 16 (4), Springer, pp. 1424–1437. Available: https://doi.org/10.1007/s13198-025-02755-y.

[12] M. Airaj, “Ethical artificial intelligence for teaching-learning in higher education,” Education and Information Technologies (2024), 29, Springer, pp. 17145–17167. Available: https://doi.org/10.1007/s10639-024-12545-x.

[13] Innovative Technologies and Learning, Proceedings of 8th ICITL International Conference, Part I (2025), W.-S. Wang, F. E. Sandnes, C.-F. Lai, T. A. Sandtrø and Y.-M. Huang (Eds.), Springer, Oslo, Norway, 398 p. Available: https://doi.org/10.1007/978-3-031-98185-2.

[14] 3 Trends in Robotics Energy Consumption (2021). Available: https://www.roboticstomorrow.com/story/2021/03/3-trends-in-robotics-energy-consumption/16385/#.

[15] D. H. Canchanya, R. A. B. Alva, V. E. P. Villa and J. H. Pariona, “Features of Implementing Tensegrity Robots: A Detailed and Innovative Review,” CMAAE International Conference on Mechanical, Aerospace and Automotive Engineering (2021), pp. 126-131. Available: https://dl.acm.org/doi/10.1145/3518781.3519194

[16] Springer Handbook of Robotics (2016), Siciliano, B. and Khatib, O. (Eds.) Springer, 2228 p. Available: https://dblp.org/db/reference/robo/robo2016.html

[17] V. Vodovozov, V. Mjadzel, and L. Rassudov, Robots in Shipbuilding Manufactures (1986), St. Petersburg: Shipbuilding, 248 p.

[18] V. Vodovozov, Z. Raud, and E. Petlenkov, “Review on braking energy management in electric vehicles,” Energies (2021), 14(15), 4477. Available: https://www.mdpi.com/1996-1073/14/15/4477.

[19] S. Hosseini and I. Hahn, “On experimental energy consumption estimation of a 6-DoF industrial UR3e robot arm manipulator in trajectory planning,” 33rd IEEE International Conference on Robot and Human Interactive Communication (2024), Pasadena, CA, USA, pp. 1430-1435. Available: https://doi.org/10.1109/RO-MAN60168.2024.10731263.

[20] I. Voysey, T. G. Thuruthel and F. Iida, “Autonomous dishwasher loading from cluttered trays using pre-trained deep neural networks,” Engineering Reports (2021), v. 3, no. 5: e12321. Available: https://doi.org/10.1002/eng2.12321.