Skip to main content

The Impact of Conversational Agents’ Language on Self-efficacy and Summary Writing

  • Conference paper
  • First Online:
Artificial Intelligence in Education (AIED 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13355))

Included in the following conference series:

  • 4678 Accesses

Abstract

This study investigated the impact of conversational agent formality on summary writing and self-efficacy in a conversation-based intelligent tutoring system. Conversational agents guided learners to learn summarization strategies in one of three conditions: a formal language, an informal language, and a mixed language condition. Results showed no significant difference in summary writing gains between groups, but learners in the informal language group achieved higher self-efficacy gains than learners in the formal language group when controlling for demographic attributes, years of English learning, prior perception of summary writing, and prior reading and summary writing proficiency. Results also indicated a negative association between self-efficacy gains and summary writing gains with a marginal significance. Implications are discussed for the design of conversational agents in the ITS.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Kim, Y., Baylor, A.L.: Research-based design of pedagogical agent roles: a review, progress, and recommendations. Int. J. Artif. Intell. Educ. 26(1), 160–169 (2015). https://doi.org/10.1007/s40593-015-0055-y

    Article  Google Scholar 

  2. Chattaraman, V., Kwon, W.-S., Gilbert, J.E., Ross, K.: Should AI-based, conversational digital assistants employ social- or task-oriented interaction style? A task-competency and reciprocity perspective for older adults. Compt. Hum. Behav. 90, 315–330 (2019). https://doi.org/10.1016/j.chb.2018.08.048

    Article  Google Scholar 

  3. Ginns, P., Martin, A.J., Marsh, H.W.: Designing instructional text in a conversational style: a meta-analysis. Educ. Psychol. Rev. 25(4), 445–472 (2013). https://doi.org/10.1007/s10648-013-9228-0

    Article  Google Scholar 

  4. Graesser, A.C., Li, H., Forsyth, C.: Learning by communicating in natural language with conversational agents. Curr. Dir. Psychol. Sci. 23, 374–380 (2014). https://doi.org/10.1177/0963721414540680

    Article  Google Scholar 

  5. Graesser, A.C., McNamara, D.S., Cai, Z., Conley, M., Li, H., Pennebaker, J.: Coh-metrix measures text characteristics at multiple levels of language and discourse. Elem. Sch. J. 115, 210–229 (2014). https://doi.org/10.1086/678293

    Article  Google Scholar 

  6. Li, H., Cheng, Q., Yu, Q., Graesser, A.C.: The role of peer agent’s learning competency in trialogue-based reading intelligent systems. In: Conati, C., Heffernan, N., Mitrovic, A., Verdejo, MFelisa (eds.) AIED 2015. LNCS (LNAI), vol. 9112, pp. 694–697. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-19773-9_94

    Chapter  Google Scholar 

  7. Li, H., Graesser, A.: Impact of pedagogical agents’ conversational formality on learning and engagement. In: André, E., Baker, R., Hu, X., Rodrigo, M.M.T., du Boulay, B. (eds.) AIED 2017. LNCS (LNAI), vol. 10331, pp. 188–200. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-61425-0_16

    Chapter  Google Scholar 

  8. Li, H., Graesser, A.C.: Impact of conversational formality on the quality and formality of written summaries. In: Bittencourt, I.I., Cukurova, M., Muldner, K., Luckin, R., Millán, E. (eds.) AIED 2020. LNCS (LNAI), vol. 12163, pp. 321–332. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-52237-7_26

    Chapter  Google Scholar 

  9. Li, H., Graesser, A.C.: The impact of conversational agents’ language on summary writing. J. Res. Technol. Educ. (2021). https://doi.org/10.1080/15391523.2020.1826022

    Article  Google Scholar 

  10. Lin, L., Ginns, P., Wang, T., Zhang, P.: Using a pedagogical agent to deliver conversational style instruction: what benefits can you obtain? Comput. Educ. 143 (2020)

    Google Scholar 

  11. Mayer, R.E.: Designing multimedia instruction in anatomy: an evidence-based approach. Clin. Anat. 33, 2–11 (2020). https://doi.org/10.1002/ca.23265

    Article  Google Scholar 

  12. Reichelt, M., Kämmerer, F., Niegemann, H.M., Zander, S.: Talk to me personally: personalization of language style in computer-based learning. Comput. Hum. Behav. 35, 199–210 (2014). https://doi.org/10.1016/j.chb.2014.03.005

    Article  Google Scholar 

  13. Riehemann, J., Jucks, R.: Address me personally!: on the role of language styles in a MOOC. J. Comput. Assist. Learn. 34, 713–719 (2018). https://doi.org/10.1111/jcal.12278

    Article  Google Scholar 

  14. Shell, D.F., Colvin, C., Bruning, R.H.: Self-efficacy, attribution, and outcome expectancy mechanisms in reading and writing achievement: grade-level and achievement-level differences. J. Educ. Psychol. 87(3), 386–398 (1995). https://doi.org/10.1037/0022-0663.87.3.386

    Article  Google Scholar 

Download references

Acknowledgments

This work was funded by the Institute of Education Sciences (Grant No. R305C120001).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Haiying Li .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Li, H., Cheng, F., Wang, G., Graeser, A. (2022). The Impact of Conversational Agents’ Language on Self-efficacy and Summary Writing. In: Rodrigo, M.M., Matsuda, N., Cristea, A.I., Dimitrova, V. (eds) Artificial Intelligence in Education. AIED 2022. Lecture Notes in Computer Science, vol 13355. Springer, Cham. https://doi.org/10.1007/978-3-031-11644-5_48

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-11644-5_48

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-11643-8

  • Online ISBN: 978-3-031-11644-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics