Brains and Bots: EEG Insights into Moral Learning with Social Robots and Text-Based Methods

Abstract

Moral education is essential for the development of ethical reasoning and prosocial behavior, but conventional approaches do not have the interactivity required to capture learners. This research compared the efficacy of robot-mediated discussion with text-based reading to deliver moral concepts through an experiment involving a control group for the purpose of comparison. Seventy-five individuals (between the ages of 18 and 30) were randomly divided into one of the three groups. A moral tale covering responsibility and fairness was employed as the lesson content. Moral understanding was measured through a comprehension test, while EEG measures (Alpha/Theta, Beta/Theta, Gamma/Beta) registered cognitive investment. Instructional groups in both conditions outperformed control significantly in comprehension. EEG data showed greater cognitive investment in instructional conditions relative to control, although no neural distinction was observed between robot and reading conditions. These findings underscore that interactive, learner-centered approaches—be they technology-mediated or conventional—can bolster moral learning, justifying the blending of various pedagogical tools in moral education. From a human factors point of view, the findings lend support to the incorporation of heterogeneous, learner-sensitive tools to support different preferences and maximize instructional effectiveness in moral education settings.

Keywords

moral education cognitive engagement EEG social robots instructional methods human factors

Get full access to this article

View all access options for this article.

References

Belpaeme

Kennedy

Ramachandran

Scassellati

Tanaka

(2018). Social robots for education: A review. Science Robotics, 3(21), eaat5954. https://doi.org/10.1126/scirobotics.aat5954

Berkowitz

M. W.

(2011). What works in values education. International Journal of Educational Research, 50(3), 153–158. https://doi.org/10.1016/j.ijer.2011.07.003

Boone

W. J.

Staver

J. R.

Yale

M. S.

(2014). Rasch analysis in the human sciences. Springer.

Bouten

C. V.

Koekkoek

K. T.

Verduin

Kodde

Janssen

J. D

. (1997). A triaxial accelerometer and portable data processing unit for the assessment of daily physical activity. IEEE Transactions on Biomedical Engineering, 44(3), 136–147.

Chauhan

Chaudhary

Bhavsar

Mombaur

Dutt

(2024). Exploring moral learning through Bhagavad Gita: A comparative study of social robot interaction and traditional reading [Conference session]. International Conference on Social Robotics. https://doi.org/10.1007/978-981-99-6809-0_20

Cohen

(1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.

Engel

A. K.

Fries

(2010). Beta-band oscillations–Signalling the status quo? Current Opinion in Neurobiology, 20(2), 156–165. https://doi.org/10.1016/j.conb.2010.02.015

Faul

Erdfelder

Lang

A. G.

Buchner

(2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39(2), 175–191. https://doi.org/10.3758/BF03193146

Field

(Ed.). (2018). Discovering statistics using IBM SPSS statistics (5th ed.). Sage Publications.

10.

Kahn

P. H.

Gary

H. E.

Shen

(2013). Children’s social relationships with current and near-future robots. Child Development Perspectives, 7(1), 32–27. https://doi.org/10.1111/cdep.12011

11.

Kohlberg

(1984). Essays on moral development, Vol. II: The psychology of moral development. Harper & Row.

12.

Kory-Westlund

J. M.

Breazeal

(2019). A long-term study of young children’s rapport, social emulation, and language learning with a peer-like robot playmate in preschool. Frontiers in Robotics and AI, 6, 81. https://doi.org/10.3389/frobt.2019.00081

13.

Lapsley

D. K.

Narvaez

(2006). Character education. In Damon

Lerner

R. M.

(Eds.), Handbook of child psychology (Vol. 4, 6th ed., pp. 248–296). Wiley.

14.

LaRosa

Danks

. (2018, December). Impacts on trust of healthcare AI. In Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society (pp. 210–215).

15.

Luo

Wang

(2020). The EEG-based assessment of cognitive workload in real-time: A review. IEEE Transactions on Human-Machine Systems, 50(6), 564–575. https://doi.org/10.1109/THMS.2020.3033919

16.

Magosso

Ricci

Ursino

(2019). Alpha rhythm modulation during visual attention: A model-based neurofeedback approach. Frontiers in Computational Neuroscience, 13, 10. https://doi.org/10.3389/fncom.2019.00010

17.

Narvaez

(2001). Moral text comprehension: Implications for education and research. Journal of Educational Psychology, 30(1), 43–54. https://doi.org/10.1080/03057240120033802

18.

Rest

J. R.

Narvaez

Bebeau

M. J.

Thoma

S. J.

(1999). Postconventional moral thinking: A neo-Kohlbergian approach. Lawrence Erlbaum Associates.

19.

Ricci

Hodak

. (2002, September). Current issues and research related to the development and delivery of interactive electronic technical manuals. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting (Vol. 46, No. 5, pp. 632–635). Sage CA: Los Angeles, CA: SAGE Publications.

20.

Roy

R. N.

Bonnet

Charbonnier

Campagne

(2019). Mental fatigue and working memory load estimation: Interaction and implications for EEG-based passive BCI. Frontiers in Neuroscience, 13, 134. https://doi.org/10.3389/fnins.2019.00134

21.

Shaffer

Ginsberg

J. P.

(2017). An overview of heart rate variability metrics and norms. Frontiers in Public Health, 5, 258. https://doi.org/10.3389/fpubh.2017.00258