CoGnosis

Revista de Educación

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Emotions and Technology in Education: a Review

Focused on Motivation

Emoções e Tecnologia na Educação: uma Revisão com Foco na

Motivação

Emociones y tecnología en la educación: una revisión centrada

en la motivación

AUTOR:

Hernan Isaac Ocana Flores

School of Information Technology, The University of Queensland, Brisbane

Australia

kaliman.hernan@gmail.com

https://orcid.org/0000-0001-6258-3828

Fecha de recepción: 2025-02-08

Fecha de aceptación: 2025-04-14

Fecha de publicación: 2025-04-24

ABSTRACT

This study analyzes the impact of emotional intelligence on motivation, subjective well-

being, academic burnout, and the use of affective technologies within human -computer

interaction (HCI) in these settings. Through a systematic literature review, the study

explores the relationships between emotions, educational technologies, and academic

performance, highlighting the importance of considering the emotional dimension i n the

design of learning environments. The author bases a strong postulate on recent findi ngs

on the educational and psychological space that showed that students with higher levels

of emotional intelligence exhibit greater intrinsic motivation, lower academic burnout,

and higher levels of subjective well-being. At the same time, how technologies allow the

possibility for pedagogical and psychological strategies to be adapted improving the

personalization of learning and therefore aiding to a desired educational performance .

KEYWORDS: Emotional intelligence; affective technologies; human-computer interaction;

motivation; educational ethics.

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RESUMO

Este estudo analisa o impacto da inteligência emocional na motivação, no bem-estar

subjetivo, no esgotamento acadêmico e no uso de tecnologias afetivas na interação

humano-computador (IHC) nesses contextos. Por meio de uma revisão sistemática da

literatura, o estudo explora as relações entre emoções, tecnologias educacionais e

desempenho acadêmico, destacando a importância de considerar a dimensão emocional

no design de ambientes de aprendizagem. O autor baseia-se em fortes postulados em

descobertas recentes no âmbito educacional e psicológico, que demonstraram que alunos

com maiores níveis de inteligência emocional apresentam maior motivação intrínseca,

menor esgotamento acadêmico e maiores níveis de bem-estar subjetivo. Ao mesmo

tempo, analisa como as tecnologias permitem a adaptação de estratégias pedagógicas e

psicológicas, melhorando a personalização da aprendizagem e, portanto, contribuindo

para o desempenho educacional desejado.

PALAVRAS-CHAVE: Inteligência emocional; tecnologias afetivas; interação humano-

computador; motivação; ética educacional.

RESUMEN

Este estudio analiza el impacto de la inteligencia emocional en la motivación, el bienestar

subjetivo, el agotamiento académico y el uso de tecnologías afectivas en la interacción

persona-ordenador (HCI) en estos entornos. A través de una revi si ón sistemática de la

literatura, el estudio explora las relaciones entre las emociones, las tecnologías

educativas y el rendimiento académico, destacando la importancia de considerar la

dimensión emocional en el diseño de entornos de aprendizaje. El autor basa un sólido

postulado en hallazgos recientes en el ámbito educativo y psicológico que demuestran

que los estudiantes con mayores niveles de inteligencia emocional presentan mayor

motivación intrínseca, menor agotamiento académico y mayores niveles de bienestar

subjetivo. Al mismo tiempo, analiza cómo las tecnologías permiten adaptar las

estrategias pedagógicas y psicológicas para mejorar la personalización del a prendizaje y,

por lo tanto, contribuir al rendimiento educativo deseado.

PALABRAS CLAVE: Inteligencia emocional; tecnologías afectivas; interacción persona -

ordenador; motivación; ética educativa.

1. INTRODUCTORY FRAMEWORK

1.1 Contextualizing Emotion in Educational Technologies

Traditional methods for education had often overlooked the role of human

emotion especially as external factors to the cognitive process (Sarnou & Sarnou,

2023). However, during the last decade with the raise of technology and

development of mental models through in which the users perceive and interact

with it had led to the establishment of HIC Human Computer Interaction (Ocaña

et al., 2024; Luna et al., 2025). Alongside with the raise of new technological

applications that had aid various sectors such as is education is worth

mentioning that the emotions have a direct impact over attention, memory,

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motivation and self-check process, key elements for an effective learner in the

digital age (Villamarín et al., 2023).

Thus, this paper presents an overview not only for HCI and emotional learning

but also investigates emotional intelligence. This is defined by Zhao et al. (2022)

as the capacity to self-regulating the internal and external emotions. The purpose

of this focus is that is been identified as one of the variables of the academic

performance. (Lee et al., 2022; Pacheco-Mendoza et al., 2023; Alenezi, 2024).

1.2 Human-Computer Interaction as a Framework for Emotional Responsivenes s

Previous studies have shown that emotion-sensitive technologies possess

methodological challenges (McStay, 2020; Roemmich & Andalibi, 2021; Härting et

al., 2021). At the methodological level, there is a fragmentation of knowledge that

is derived from the multiplicity of approaches (Flores, 2025) for example,

engineering, psychology, pedagogy. Therefore, both technological and

psychological studies have opted for systematic reviews to build more integrated

theoretical frameworks (Ding & Zou, 2024). Human–computer interaction is no

longer confined to usability alone but has expanded to encompass the quality of

user experience (Nicolescu & Tudorache, 2022; Kivijärvi & Pärnänen, 2023), in

different levels (Guallichico et al., 2023; Ocaña et al., 2022) and subjects (Bazurto

Palma, & Ocaña Garzón, 2023).

According to Kim et al. (2022), affective responses play a crucial role in shaping

behavior, fostering trust, and sustaining user engagement. Therefore, developing

emotion recognition functionalities enables organizations to more accurately

analyze both user interaction behaviors and learners’ emotional states (Flores,

2023). Researchers also suggest that such systems possess real-time adaptive

capabilities, allowing for dynamic adjustments in content pacing and feedback

intensity, tailored to the emotional and cognitive conditions of each individual

(Kaklauskas et al., 2022; Liu & Yin, 2024; Glickman & Sharot, 2025).

Similar models in emotion recognition technology can also be employed to

enhance learners' self-regulated learning capacity, support cognitive processing,

and minimize distractions (Chen et al., 2024; Ngo et al., 2024; Shi, 2025).

Thereby promoting the development of more responsive educational environments

and advancing the modes of interaction within broader social contexts.

1.3 Literature Review

Originating from the control-value theory of achievement emotions, Pekrun (2024)

proposed the hypothesis that learners’ appraisals of task control and value

directly influence the formation of emotions, thereby impacting core cognitive

processes such as attention, motivation, and memory. This theoretical model

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continues to serve as a foundational framework for contemporary research on

affective learning across diverse educational contexts, including human-computer

interaction systems (Flores & Luna, 2024), Yin et al., 2024; Namaziandost &

Rezai, 2024; Yuvaraj et al., 2025).

A prominent line of empirical research in this domain involves the application of

Intelligent Tutoring Systems (ITS) that adapt instructional strategies based on

learners’ emotional states (Lin et al., 2023). Systems such as AutoTutor and

Affective AutoTutor incorporate multimodal emotion detection technologies

through the analysis of facial expressions, linguistic features, and interaction

patterns (Fernández-Herrero, 2024; Khediri et al., 2024; Sirisha et al., 2025).

These interventions have demonstrated substantial effects, with effect sizes

reaching d ≈ 0.8, which is equivalent to an improvement of nearly one letter grade

compared to traditional instructional methods (Chen et al., 2025).

Technical approaches in this domain often integrate computer vision, voice

analysis, and physiological sensors (Al-Saadawi et al., 2024; Flores & Luna,

2024), facilitating the advancement of Affective Learning Analytics (Arockia et al.,

2025). These tools enable real-time monitoring of fluctuations in learner

engagement and emotional states (Aly, 2024), thereby providing educational

institutions with additional actionable insights to inform instructional practices.

However, these technological advances come with significant challenges. Emotion

recognition models are particularly susceptible to dataset bias, especially when

trained on homogeneous populations, which results in inaccurate or unfair

predictions in diverse educational settings (Mattioli & Cabitza, 2024; Shah &

Sureja, 2025). Moreover, the black box nature of AI systems raises concerns

regarding transparency and trustworthiness; when adaptive decisions lack clear

explanations, educators often express skepticism about integrating such

technologies into real-world educational contexts (Flores, 2023; Salloum, 2024;

Chaudhary, 2024; Marey et al., 2024).

Furthermore, a longitudinal study researching similar technological systems

mentioned previously was conducted over a period of 2 last decades. In this kind

of system, the teaching method changes based on the stimulus found on users

adjusting lessons, offering support when they detect frustration or speeding up

the pace when they identify boredom (Schmitz-Hübsch, et al, 2022). This adaptive

capacity has been shown to significantly improve student comprehension and

engagement across different utilization of digital system across in educational

settings. This finding is crucial as it demonstrates that the relationship between

emotion and performance is highly individual. However, according to Schmitz-

Hübsch, et al, (2022) states that not all students learn best under the same

emotional conditions, so affective tutoring systems must be able to adapt to

individual differences and not respond in a standardized way to frustration,

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boredom, or satisfaction. According to Chaudhary et al. (2024), personalizing

tutoring based on emotional profile can improve learning effectiveness and help

students develop stronger coping strategies.

This correlates with the study by Le et al. (2024) as it provides empirical evidence

on the importance of emotional intelligence as a protective factor against

academic burnout and as a driver of motivation and subjective well-being.

Students with high EI are not only better able to regulate their emotions, but also

report greater satisfaction with their academic life and a lower predisposition to

emotional exhaustion additionally aligned with the framework for emotional

target done by Flores & Luna (2024).

Thus, this study is structured around four key objectives:

1. To identify how emotional intelligence is being used as a predictive or

modulating variable in technology-mediated educational environments.

2. To analyze affective educational systems and emotional detection

frameworks that integrate psychophysiological and behavioral signals.

3. To explore the relationship between emotions, motivation, academic

burnout, and subjective well-being, with an emphasis on university settings.

4. To reflect on the ethical and socio technical risks involved in incorporating

emotion-sensitive technologies into teaching and learning processes.

2. METHODOLOGY

2.1 Scoping literature review

In the methodology of this study, an in-depth literature search will be positioned

as the core strategy of this methodology (Arksey & O’Malley, 2005). This aims at

achieving the goal of critically compiling, examining and interpreting the existing

information within the academic arena concerning the interrelation between the

emotional intelligence, the technologies involved and wellbeing which these would

be capable of creating within a setting of education. Thus, according to this way,

not only the patterns of evidence can be found but also possible gaps and ethical

dilemmas.

2.2 Search Queries

The review followed a systematic search protocol across academic databases

including ERIC, Scopus, and IEEE:

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● "emotion recognition" AND "education" AND "subjective well-being"

● "affective computing" AND "learning"

● "emotion-aware tutoring systems" OR "affective feedback" AND "student

engagement"

Inclusion criteria limited studies to those published in the last decade written in

English, peer-reviewed, and explicitly addressing emotion recognition in K–12,

higher education, or digital learning contexts.

Studies in English and Spanish were included if they met the following

conditions:

● Described or evaluated AI systems designed to detect or respond to learner

emotion.

● Reported educational applications (e.g., tutoring, learning analytics,

classroom tools).

● Provided empirical or validated evidence, either quantitative or qualitative.

Excluded were:

● Studies in therapeutic or clinical psychology unrelated to learning.

● Purely conceptual or editorial articles lacking methodology.

● Applications in entertainment, gaming, or unrelated sectors.

2.3 Data selection

After deduplication in Endnote, data screening and extraction focused on the

following elements:

● Type of AI/affective technology used.

● Context of implementation (e.g., online, face-to-face, hybrid).

● Sample demographics, if reported.

2.4 Data analysis

The final review included 21 papers after applying the aforementioned inclusion

and exclusion criteria. Thematic analysis was used to derive inductively emerging

themes which are reported in the next section.

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3. RESULTS

3.1 Technological Approaches to Emotion Detection in Education

In this paper the research has shown that correlation between emotion and

academic performance is very complicated and largely dependent on individuals

than initially thought. The most widely used theoretical perspective is the control-

value theory by Pekrun (2024), which sets out that the student perceives the

perceived control and value of a task identifies which emotions crop up during

learning, which influence the most important cognitive mechanisms of attention,

memory, as well as motivation.

Moreover, human centered technologies targeted to emotions such as the

previously mentioned AutoTutor and Affective AutoTutor, have had the capacity to

change their teaching methods according to the emotional state the user is being

perceive by the system, producing significant increases in understanding and

performance of students (Chen et al., 2025; Lin et al., 2023). Multimodal

technologies (consist of computer vision, voice analysis, physiological sensors)

allow the real-time identification of the changes in the emotional state of

students, giving teachers more data to instantaneously modify teaching (Al-

Saadawi et al., 2024; Flores & Luna, 2024; Arockia et al., 2025).

However, it is worth mentioning that the collective research suggests that there is

an increasing evidence on the effectiveness of affective interventions in user

centered educational designs as the aggregate results demonstrate that the effect

sizes as the longitudinal studied shown an increase of roughly 2 points, which is

the same as an increase of an entire grade improvement as compared to

conventional approaches The study conducted by Schmitz-Hübsch et al. (2022)

that shows students have better understanding and interest in what they learn

with the possibilities of adjusting to their emotional status in the digital systems,

whether it is providing assistance in cases of frustration detection or speeding up

the lessons in case they become bored.

Additionally, as per usual with emotion detection systems, this requires data to

be gathered and in most cases the data collected is live (in the moment the user

engages with the system). There's, also worth mentioning that the reliability of the

system varies so these systems are not totally independent regardless of their

automation, many still have a generalization that could potentially affect certain

individuals. For example, when used with culturally diverse communities,

training biases in emotion recognition models can lead to erroneous or unjust

forecasts (Mattioli & Cabitza, 2024; Shah & Sureja, 2025). The system would

require proper iteration therefore the intervention of human computer

interaction in which the user center cycle will adapt accordingly through the

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variations, eliminating the issue with the black box model, and strengthening the

trust among users as teaching staff require clear feedback form the system to

justify and adapt their pedagogical adaptations and models (Flores, 2023;

Salloum, 2024; Chaudhary, 2024).

Nevertheless, it is required to mention that the discussed systems in this

research are declared to be working with focused groups and that not every

student learns better when he/she is in the same emotional state. There are

types of people who are more likely to be positively affected by the pressure-

thriving environments or negative emotions, whereas others have to be placed in

weaker positions of emotional charge to work the best. These are also consistent

with Chaudhary et al. (2024), who argue that the personalization of learning

according to individual emotional profile is instrumental in ensuring the optimal

learning process. Affective systems are not supposed to provide pre-designed

reactions but acknowledge individualistic emotional pathways of students and

respond to them.

3.3. Protective Factor of Emotional Intelligence

The latter study by Le et al. (2024) supplements these findings by introducing the

evidence about higher emotional intelligence (EI) control rates of the students and

lower burnout risks and more positive emotions about the university experience.

EI is a shield against academic stress and a stimulus to long-term motivation and

well-being. These findings underline a need to pay attention not only to the real -

time tracking of other emotions but also to the deliberate acquisition of socio-

emotional skills. Moreover, this solution correlates with the findings of Flores &

Luna (2024), who state that developing affective systems has to be incorporated

into the ethical and emotionally accountable pedagogical practices, and the

emotional control of students in those systems is a clear goal of the learning

process, rather than one of the inputs in the technology setting.

3.4. Derived conclusive research points

The combined analysis of the reviewed studies suggests that the effectiveness of

affective tutoring systems depends on three key elements:

• Accurate and multimodal detection of emotions in specific scenarios where

motivators can be boosted

• The capacity for a personalized education system based on individual

differences.

• The integration of emotional development strategies, that aid emotional

intelligence, strengthen student autonomy and resilience.

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These elements not only optimize adaptive learning but also contribute to the

construction of more inclusive, ethical, and emotionally sustainable educational

environment.

4. CONCLUSION

This integrated review demonstrates that both emotional intelligence (EI) and

affective technologies in education share a central objective: to improve the

learning experience from a comprehensive approach that recognizes emotion as

an essential component of the educational process. The findings show that

students with high levels of EI not only exhibit greater motivation and subjective

well-being, but also manage to cushion the effects of academic burnout, thereby

improving their performance and satisfaction in the university context.

For their part, emotional detection technologies such as intelligent tutoring

systems and affective analysis platforms contribute to personalizing teaching,

adapting in real time to students' emotional needs. However, their positive impact

is only sustainable if they are integrated into pedagogical models that promote

self-regulation, active participation, and student autonomy.

This study confirms that the combination of emotional intelligence, intrinsic

motivation, and emotionally responsive technology can enhance not only learning

but also students' psychological well-being. However, it cautions that this

integration must be carried out critically, responsibly, and with solid ethical

principles that guarantee privacy protection and algorithmic transparency.

4.1. Recommendations

This scoping review shows that 2 performative factors: emotional intelligence and

affective technologies in learning can be used to have a common objective of

enhancing the learning experiences. The results indicate that high-EI students do

not only have a high level of motivation and subjective well-being, but also

succeed in offsetting academic burnout, which in turn means that these skill sets

allows them to perform better and be more satisfied in the university environment

On their part, the emotional recognition tools like the intelligent tutoring systems

and affective analysis platforms are also helping in the personalization of

teaching, adapting based on real-time emotional requirements of the students.

Nonetheless, their positive effects are only feasible under strict controlled

conditions in which they should be incorporated into pedagogical models by the

educator that encourage self-regulation, active involvement, and student

independence. This research provides a point into supporting the individual's

emotional intelligence through the use of technology that can not only improve

the learning outcomes but the psychological state of students, as well. However,

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it is worth mentioning that such integration should be done in a critical,

responsible and strong ethical framework especially in regards to technological

use, ensuring that the users involved are aware of which date and performance

targets are being used for sustaining the system.

6. CONFLICT OF INTEREST STATEMENT

The author declares no conflicts of interest. No funding or support was

received from any organization or entity that could influence the content

of this work.

7. AUTHOR’S CONTRIBUTION

Author

Conceptualization, Data curation, Formal analysis, Investigation,

Methodology, Resources, Software, Validation, Visualization, Writing

– original draft, Writing – review & editing

8. REFERENCES

Alenezi, A. (2024). The effect of emotional intelligence on higher education: A pilot study on the

interplay between artificial intelligence, emotional intelligence, and e -learning. Multidisciplinary

Journal for Education, Social and Technological Sciences, 11(2), 51-

77.https://doi.org/10.4995/muse.2024.21367

Al-Saadawi, H. F. T., Das, B., & Das, R. (2024). A systematic review of trimodal affective

computing approaches: Text, audio, and visual integration in emotion recognition and sentiment

analysis. Expert Systems with Applications, 124852.

https://doi.org/10.1016/j.eswa.2024.124852

Aly, M. (2024). Revolutionizing online education: Advanced facial expression recognition for real -

time student progress tracking via deep learning model. Multimedia Tools and Applic ations, 1-40.

https://doi.org/10.1007/s11042-024-19392-5

Arksey, H., & O’Malley, L. (2005). Scoping studies: towards a methodological framework.

International Journal of Social Research Methodology, 8(1), 19–32.

https://doi.org/10.1080/1364557032000119616

Arockia, V. J., Vettriselvan, R., Rajesh, D., Velmurugan, P. R. R., & Cheelo, C. (2025). Leveraging

AI and Learning Analytics for Enhanced Distance Learning: Transformation in Education. In AI

and Learning Analytics in Distance Learning (pp. 179-206). IGI Global Scientific Publishing.

https://doi.org/10.4018/979-8-3693-7195-4.ch008

Bazurto Palma, C. J., & Ocaña Garzón, M. (2023). The use of a virtual environment to improve

students’ Listening skills: a learning analytics approach. Ciencia Latina Revista Científica

Multidisciplinar, 7(2), 2937-2953. https://doi.org/10.37811/cl_rcm.v7i2.5537

Chaudhary, G. (2024). Unveiling the black box: Bringing algorithmic transparency to AI. Masaryk

University Journal of Law and Technology, 18(1), 93-122.

Chen, J., Mokmin, N. A. M., & Qi, S. (2025). Generative AI-powered arts-based learning in middle

school history: Impact on achievement, motivation, and cognitive load. The Journal of Educational

Research, 1-13. https://doi.org/10.1080/00220671.2025.2510395

138

e-ISNN 2588 – 0578 Vol. X, Edición Especial, abril, 2025

Chen, S., Cheng, H., & Huang, Y. (2024). Emotion Recognition in Self-Regulated Learning:

Advancing Metacognition through AI-Assisted Reflections. In Trust and Inclusion in AI-Mediated

Education: Where Human Learning Meets Learning Machines (pp. 185-212). Cham: Springer

Nature Switzerland. https://doi.org/10.1007/978-3-031-64487-0_9

Ding, L., & Zou, D. (2024). Automated writing evaluation systems: A systematic review of

Grammarly, Pigai, and Criterion with a perspective on future directions in the age of generative

artificial intelligence. Education and Information Technologies, 29(11), 14151-14203.

https://doi.org/10.1007/s10639-023-12402-3

Fernández-Herrero, J. (2024). Evaluating recent advances in affective intelligent tutoring systems:

A scoping review of educational impacts and future prospects.

https://doi.org/10.3390/educsci14080839

Flores, H. I. O. (2023). Human computer interaction’s insights into the recognition of love: a

comprehensive framework. Tierra Infinita, 9(1), 228-245.

https://doi.org/10.32645/26028131.1254

Flores, H. I. O. (2025). Creating an Adaptive Voice and Language Model Capable of Emotional

Response and Self-Profiling to Emulate User Personality. Ciencia Latina Revista Científica

Multidisciplinar, 9(1), 3454-3471. https://doi.org/10.37811/cl_rcm.v9i1.16093

Flores, H. I. O., & Luna, A. (2024). AI for Psychological Profiles: Advances, Challenges, and Future

Directions. Ciencia Latina Revista Científica Multidisciplinar, 8(3), 10592-10609.

https://doi.org/10.37811/cl_rcm.v8i3.12221

Glickman, M., & Sharot, T. (2025). How human–AI feedback loops alter human perceptual,

emotional and social judgements. Nature Human Behaviour, 9(2), 345-359.

https://doi.org/10.1038/s41562-024-02077-2

Guallichico, G., Ocaña, M., Tejada, C., Bautista, C. (2023). Assessing Digital Competence Through

Teacher Training in Early Education Teachers. In: Botto-Tobar, M., Zambrano Vizuete, M., Montes

León, S., Torres-Carrión, P., Durakovic, B. (eds) Applied Technologies. ICAT 2022.

Communications in Computer and Information Science, vol 1757. Springer, Cham.

https://doi.org/10.1007/978-3-031-24978-5_6

Härting, R. C., Schmidt, S., & Krum, D. (2021). Potentials of Emotionally Sensitive Applications

Using Machine Learning. In Human Centred Intelligent Systems: Proceedings of KES -HCIS 2020

Conference (pp. 207-219). Springer Singapore. https://doi.org/10.1007/978-981-15-5784-2_17

Kaklauskas, A., Abraham, A., Ubarte, I., Kliukas, R., Luksaite, V., Binkyte-Veliene, A., ... &

Kaklauskiene, L. (2022). A review of AI cloud and edge sensors, methods, and applications for the

recognition of emotional, affective and physiological states. Sensors, 22(20), 7824-7824.

https://doi.org/10.3390/s22207824

Khediri, N., Ben Ammar, M., & Kherallah, M. (2024). A Real-time Multimodal Intelligent Tutoring

Emotion Recognition System (MITERS). Multimedia Tools and Applications, 83(19), 57759-57783.

https://doi.org/10.1007/s11042-023-16424-4

Kim, J., Yang, K., Min, J., & White, B. (2022). Hope, fear, and consumer behavioral change amid

COVID‐19: Application of protection motivation theory. International Journal of Consumer

Studies, 46(2), 558-574. https://doi.org/10.1111/ijcs.12700

Kivijärvi, H., & Pärnänen, K. (2023). Instrumental usability and effective user experience:

Interwoven drivers and outcomes of Human-Computer interaction. International Journal of

Human–Computer Interaction, 39(1), 34-51. https://doi.org/10.1080/10447318.2021.2016236

Emotion and Technologies in aid for Education: a focus in motivation

Hernan Isaac Ocana Flores

CoGnosis

Revista de Educación

e-ISNN 2588 – 0578 Vol. X, Edición Especial, abril, 2025

139

Le, U. P. N., Nguyen, A. T. T., Van Nguyen, A., Huynh, V. K., Le Bui, C. T., & Nguyen, A. L. T.

(2024, August). How Do Emotional Support and Emotional Exhaustion Affect the Relationship

Between Incivility and Students’ Subjective Well-Being?. In Disruptive Technology and Business

Continuity: Proceedings of The 5th International Conference on Business (ICB 2023) (pp. 237 -

248). Singapore: Springer Nature Singapore.

Lee, Y. F., Hwang, G. J., & Chen, P. Y. (2022). Impacts of an AI-based cha bot on college students’

after-class review, academic performance, self-efficacy, learning attitude, and motivation.

Educational technology research and development, 70(5), 1843-

1865.https://doi.org/10.1007/s11423-022-10142-8

Lin, C. C., Huang, A. Y., & Lu, O. H. (2023). Artificial intelligence in intelligent tutoring systems

toward sustainable education: a systematic review. Smart Learning Environments, 10(1), 1 -22.

https://doi.org/10.1186/s40561-023-00260-y

Liu, C. Y., & fShi, B. (2024). Affective foundations in AI-human interactions: Insights from

evolutionary continuity and interspecies communications. Computers in Human Behavior, 161,

108406. https://doi.org/10.1016/j.chb.2024.108406

Luna, A., Ocaña, M., Rodríguez-Moreno, J., & Ortíz-Colón, A. M. (2025). Establishing the

foundation for technology adoption: profiles of military students in the digital age. Frontiers in

Psychology, 16, 1593326. https://doi.org/10.3389/fpsyg.2025.1593326

Marey, A., Arjmand, P., Alerab, A. D. S., Eslami, M. J., Saad, A. M., Sanchez, N., & Umair, M.

(2024). Explainability, transparency and black box challenges of AI in radiology: Impact on patient

care in cardiovascular radiology. Egyptian Journal of Radiology and Nuclear Medicine, 55(1), 183.

https://doi.org/10.1186/s43055-024-01356-2

Mattioli, M., & Cabitza, F. (2024). Not in my face: Challenges and ethical considerations in

automatic face emotion recognition technology. Machine Learning and Knowledge Extraction, 6(4),

2201-2231. https://doi.org/10.3390/make6040109

McStay, A. (2020). Emotional AI, soft biometrics and the surveillance of emotional life: An unusual

consensus on privacy. Big Data & Society, 7(1), 2053951720904386.

https://doi.org/10.1177/2053951720904386

Namaziandost, E., & Rezai, A. (2024). Interplay of academic emotion regulation, academic

mindfulness, L2 learning experience, academic motivation, and learner autonomy in intelligent

computer-assisted language learning: A study of EFL learners. System, 125, 103419.

https://doi.org/10.1016/j.system.2024.103419

Ngo, D., Nguyen, A., Dang, B., & Ngo, H. (2024). Facial expression recognition for examining

emotional regulation in synchronous online collaborative learning. International Journal of

Artificial Intelligence in Education, 34(3), 650-669. https://doi.org/10.1007/s40593-023-00378-7

Nicolescu, L., & Tudorache, M. T. (2022). Human-computer interaction in customer service: the

experience with AI chatbots—a systematic literature review. Electronics, 11(10), 1579.

https://doi.org/10.3390/electronics11101579

Ocaña, M., García-Cañarte, A., & Rosales, M. (2024). Enhancing EFL Vocabulary Through Short

Stories: Insights from Learning Analytics. In International Conference on Intelligent Information

Technology (pp. 438-449). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-

031-87701-8_32

Ocaña, M., Almeida, E., Albán, S. (2022). How Did Children Learn in an Online Course During

Lockdown?: A Piagetian Approximation. In: Botto-Tobar, M., Cruz, H., Díaz Cadena, A.,

Durakovic, B. (eds) Emerging Research in Intelligent Systems. CIT 2021. Lecture Notes in

140

e-ISNN 2588 – 0578 Vol. X, Edición Especial, abril, 2025

Networks and Systems, vol 406. Springer, Cham. https://doi.org/10.1007/978-3-030-96046-

9_20

Pacheco-Mendoza, S., Guevara, C., Mayorga-Albán, A., & Fernández-Escobar, J. (2023). Artificial

intelligence in higher education: A predictive model for academic performance. Education

Sciences, 13(10), 990.https://doi.org/10.3390/educsci13100990

Pekrun, R. (2024). Control-value theory: From achievement emotion to a general theory of huma n

emotions. Educational Psychology Review, 36(3), 1-36. https://doi.org/10.1007/s10648-024-

09909-7

Roemmich, K., & Andalibi, N. (2021). Data subjects' conceptualizations of and attitudes toward

automatic emotion recognition-enabled wellbeing interventions on social media. Proceedings of

the ACM on Human-Computer Interaction, 5(CSCW2), 1-34. https://doi.org/10.1145/3476049

Salloum, S. A. (2024). Trustworthiness of the AI. In Artificial intelligence in education: The power

and dangers of ChatGPT in the classroom (pp. 643-650). Cham: Springer Nature Switzerland.

Sarnou, D., & Sarnou, H. (2023). Highlighting the Consequences of Ignoring Children's Emotions

in Schools: Case of 30 pupils in 3 Algerian Primary Schools. Academy Journal of Educational

Sciences, 6(1), 44-51.https://doi.org/10.31805/acjes.1091001

Schmitz-Hübsch, A., Stasch, S. M., Becker, R., Fuchs, S., & Wirzberger, M. (2022). Affective

Response Categories-Toward Personalized Reactions in Affect-Adaptive Tutoring Systems.

Frontiers in artificial intelligence, 5, 873056. https://doi.org/10.3389/frai.2022.873056

Shah, M., & Sureja, N. (2025). A comprehensive review of bias in deep learning models: Methods,

impacts, and future directions. Archives of Computational Methods in Engineering, 32(1), 255 -

267. https://doi.org/10.1007/s11831-024-10134-2

Shi, L. (2025). The integration of advanced AI-enabled emotion detection and adaptive learning

systems for improved emotional regulation. Journal of Educational Computing Research, 63(1),

173-201. https://doi.org/10.1177/07356331241296890

Sirisha, N., Mageswari, P., Raj, V. M., Kumar, S., Priya, R. V., & Ananthi, S. (2025, May). Emotion

Centric Artificial Intelligence Driven Engagement Systems for Adaptive Learning Environments

Personalized Knowledge Acquisition and Cognitive Skill Enhancement. In International

Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024) (pp. 528 -

540). Atlantis Press. https://doi.org/10.2991/978-94-6463-718-2_46

Villamarín, A., Chumaña, J., Narváez, M., Guallichico, G., Ocaña, M., & Luna, A. (2023,

November). Artificial Intelligence in the Detection of Autism Spectrum Disorders (ASD): a

Systematic Review. In International Conference on Intelligent Vision and Compu ting (pp. 21-32).

Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-71388-0_3

Yin, J., Goh, T. T., & Hu, Y. (2024). Interactions with educational chatbots: the impact of induced

emotions and students’ learning motivation. International Journal of Educational Technology in

Higher Education, 21(1), 1-23. https://doi.org/10.1186/s41239-024-00480-3

Yuvaraj, R., Mittal, R., Prince, A. A., & Huang, J. S. (2025). Affective Computing for Learning in

Education: A Systematic Review and Bibliometric Analysis. Education Sciences, 15(1), 1 -47.

https://doi.org/10.3390/educsci15010065

Zhao, G., Li, Y., & Xu, Q. (2022). From emotion AI to cognitive AI. International Journal of

Network Dynamics and Intelligence, 65-72. https://doi.org/10.53941/ijndi0101006