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Integrating Voice-Assisted Inquiry-Based Lesson into a Web Application:An Innovation in Biology Science Education

Authors

Mae Esteeh L. Sumacot

Graduate School, North Eastern Mindanao State University, Tandag City, Surigao del Sur (Philippines)

Luzminda S. Bacquial

Department of General Teacher Training, North Eastern Mindanao State University, Tandag City, Surigao del Sur (Philippines)

Article Information

DOI: 10.47772/IJRISS.2026.100600193

Subject Category: Education

Volume/Issue: 10/6 | Page No: 2612-2623

Publication Timeline

Submitted: 2026-05-27

Accepted: 2026-06-01

Published: 2026-06-20

Abstract

This study developed and validated a Voice-Assisted Inquiry-Based Web Application (VA-IBL-WA) to address the persistently least-learned competencies in Grade 10 Biology, specifically protein synthesis (S10LT-IIIa-37), mutations and protein function (S10LT-IIIe-38), and evidence for evolution (S10LT-IIIh-41). Adopting a Developmental-Evaluative Research Design guided by the ADDIE model, the study involved 96 Grade 10 students, six science teachers, and ten school administrators and ICT coordinators across three public secondary schools in the Municipality of Tago, Surigao del Sur, Philippines. Expert validation using the DepEd LRMDS evaluation tool yielded a Very Satisfactory overall mean rating of 3.57 (out of 4.0) with a total score of 132 out of 148, confirming content, instructional, and technical acceptability. Stakeholder perception surveys produced a grand mean of 4.459 (Strongly Agree) across all five evaluation dimensions. One-Way Analysis of Variance (ANOVA) revealed no significant differences in perceptions among the three respondent groups across most indicators, with the exception of format (F = 5.45, p = 0.006). Prevailing challenges identified through thematic analysis included fast voice narration, teacher training needs, time constraints, device accessibility, and voice recognition limitations. An enhanced version of the application was subsequently developed incorporating all stakeholder feedback. The findings affirm that integrating voice-assisted technology with inquiry-based learning effectively reduces cognitive load, supports multimodal comprehension, and transforms abstract biological concepts into accessible and engaging digital learning experiences.

Keywords

Voice-Assisted Web Application, Inquiry-Based Learning, Science Education, Educational Technology, Biology, Multimodal Instruction

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References

1. Adeoye, M. A. (2024). Revolutionizing education: Unleashing the power of the ADDIE model for effective teaching and learning. Jurnal Pendidikan Indonesia, 13(1), 202–209. [Google Scholar] [Crossref]

2. https://doi.org/10.23887/jpi-undiksha.v13i1.667 [Google Scholar] [Crossref]

3. Alfredo, R., Echeverria, V., Jin, Y., Yan, L., Swiecki, Z., Gašević, D., & Martinez-Maldonado, R. (2024). Human-centred learning analytics and AI in education: A systematic literature review. Computers and Education: Artificial Intelligence, 6, Article 100215. [Google Scholar] [Crossref]

4. https://doi.org/10.1016/j.caeai.2024.100215 [Google Scholar] [Crossref]

5. Alazemi, A. F. T. (2024). Formative assessment in artificial integrated instruction: Delving into the effects on reading comprehension progress, online academic enjoyment, personal best goals, and academic mindfulness. Language Testing in Asia, 14, Article 44. https://doi.org/10.1186/s40468-024-00319-8 [Google Scholar] [Crossref]

6. Aligo, J. A., & Prudente, M. S. (2025). Effectiveness of digital learning materials on students' achievement in science: A meta-analysis. Journal of Global Education and Research, 9(1), 55–72. https://digitalcommons.usf.edu/jger/vol9/iss1/4/ [Google Scholar] [Crossref]

7. Bautista, R. G., & Santos, L. M. (2025). Contextualized digital inquiry modules: Enhancing critical thinking in Grade 10 Science. Philippine Journal of Science Education, 18(2), 112–125. [Google Scholar] [Crossref]

8. Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa [Google Scholar] [Crossref]

9. Bruner, J. S. (1961). The act of discovery. Harvard Educational Review, 31(1), 21–32. [Google Scholar] [Crossref]

10. Butler, D. (2024). Voice assistants in education: Exploring children's learning with smart speakers in classrooms. Learning, Media and Technology, 49(2), 145–162. [Google Scholar] [Crossref]

11. https://doi.org/10.1080/1475939X.2024.2311779 [Google Scholar] [Crossref]

12. Butler, D., & Starkey, L. (2024). OK Google, help me learn: An exploratory study of voice-activated artificial intelligence in the classroom. Technology, Pedagogy and Education, 33(2), 135–148. https://doi.org/10.1080/1475939X.2023.2289947 [Google Scholar] [Crossref]

13. Castro, A. J. M., & Lapiras, M. B. (2024). Revealing the lived experiences of Chemistry teachers in Davao City on online inquiry-based learning: A phenomenology. International Multidisciplinary Research Journal, 6(2), 51–63. [Google Scholar] [Crossref]

14. Conklin, S., & Barreto, D. (2023). Student perceptions of Quality Matters standards on engagement and learning. American Journal of Distance Education, 39(1), 1–15. [Google Scholar] [Crossref]

15. https://doi.org/10.1080/08923647.2023.2242734 [Google Scholar] [Crossref]

16. Darling-Hammond, L., Flook, L., Cook-Harvey, C., Barron, B., & Osher, D. (2020). Implications for educational practice of the science of learning and development. Applied Developmental Science, 24(2), 97–140. https://doi.org/10.1080/10888691.2018.1537791 [Google Scholar] [Crossref]

17. Dela Cruz, M. A. (2024). Interactive web tools and student engagement in Philippine biology classrooms. Asia Pacific Journal of Educators and Education, 39(1), 45–58. [Google Scholar] [Crossref]

18. Department of Education. (2009). Guidelines on the management and utilization of existing multimedia materials (DepEd Order No. 62, s. 2009). Republic of the Philippines. [Google Scholar] [Crossref]

19. Department of Education. (2016). K to 12 curriculum guide: Science (Grades 1–10). Republic of the Philippines. https://www.deped.gov.ph [Google Scholar] [Crossref]

20. Department of Education. (2017). ICT in Education Masterplan 2020. Republic of the Philippines. https://www.deped.gov.ph [Google Scholar] [Crossref]

21. Feng, S., Halpern, B. M., Kudina, O., & Scharenborg, O. (2024). Towards inclusive automatic speech recognition: Quantifying bias across gender, age, regional & non-native accents. Computer Speech & Language, 83, 101567. https://doi.org/10.1016/j.csl.2023.101567 [Google Scholar] [Crossref]

22. Fütterer, T., Hoch, E., & Stürmer, K. (2024). Rethinking professional development: Development and evaluation of an evidence-based online PD course on the effective use of technology in the classroom. Education Sciences, 15(2), 205. https://doi.org/10.3390/educsci15020205 [Google Scholar] [Crossref]

23. Giam, N. M., Doc, N. V., Nam, N. T. H., Giang, N. T. H., & Thanh, N. T. (2023). Measures to develop the ability to self-study Chemistry for high school students with the support of AI chatbot. Journal of Education for Sustainable Innovation, 1(1), 49–60. https://doi.org/10.56916/jesi.v1i1.519 [Google Scholar] [Crossref]

24. Goksel, N., & Bozkurt, A. (2023). Artificial intelligence in education: Current insights and future perspectives. In Handbook of Research on AI-Based Technologies and Applications in the Era of the Metaverse (pp. 1–24). IGI Global. [Google Scholar] [Crossref]

25. Hamedi, W. H. W., Ali, F. D. A., Abdullah, W. Y., Ab Hamid, H., Shuhaimi, N. I. M., & Amir, M. M. (2025). AI as a digital scaffold: An integrative review of Vygotsky's zone of proximal development in modern education. International Journal of Modern Education, 7(26), 579–589. [Google Scholar] [Crossref]

26. https://doi.org/10.35631/IJMOE.726043 [Google Scholar] [Crossref]

27. Huda, M., & Nursa'adah, F. P. (2024). Implications of Bruner's cognitive theory on elementary school education in the 21st century. Klasikal: Journal of Education, Language Teaching and Science, 6(3), 498–510. [Google Scholar] [Crossref]

28. Inoferio, H. V., Espartero, M. M., & Chavez, J. V. (2025). Coping with math anxiety and lack of confidence through AI-assisted learning. Environment and Social Psychology, 10(1), 1–12. https://doi.org/10.54517/esp.v10i1.2464 [Google Scholar] [Crossref]

29. Ismail, A. (2020). A proposed professional learning communities model for Malaysian schools: Using a design development research method. International Journal of Innovation, Creativity and Change, 13(1), 623–638. [Google Scholar] [Crossref]

30. Kannampallil, T., Ronneberg, C. R., Wittels, N. E., Kumar, V., Lv, N., Smyth, J. M., Gerber, B. S., Kringle, E. A., Johnson, J. A., Yu, P., Steinman, L. E., Ajilore, O. A., & Ma, J. (2022). Design and formative evaluation of a virtual voice-based coach for problem-solving treatment: Observational study. JMIR Formative Research, 6(8), e38092. https://doi.org/10.2196/38092 [Google Scholar] [Crossref]

31. Kotsis, K. T. (2024). Integrating ChatGPT into inquiry-based science learning: Enhancing engagement and conceptual understanding. European Journal of Education and Pedagogy, 5(2), 38–47. https://doi.org/10.24018/ejedu.2024.5.2.891 [Google Scholar] [Crossref]

32. Laid, S. M. T., & Adlaon, M. S. (2025). A systematic review of innovative teaching strategies in science: Exploring hands-on learning, technology integration, and student-centered approaches. Acta Pedagogia Asiana, 4(2), 101–114. [Google Scholar] [Crossref]

33. Lee, H., & Kim, J. (2020). AI voice assistants as virtual lab instructors in Chemistry education. Journal of Chemical Education, 97(6), 1653–1661. [Google Scholar] [Crossref]

34. Manalo, D., & Domingo, R. (2022). Voice-assisted mobile learning applications and student performance in Chemistry. Asia Pacific Journal of Educational Technology, 15(2), 102–116. [Google Scholar] [Crossref]

35. Mangali, G. R., Adilah, N., & Balagtas, M. (2025). Mitigating biology concept deficiencies through contextualized classroom instruction. SAR Journal, 8(3), 258–265. https://doi.org/10.18421/SAR83-04 [Google Scholar] [Crossref]

36. Mayer, R. E. (2005). The Cambridge handbook of multimedia learning. Cambridge University Press. [Google Scholar] [Crossref]

37. Mayer, R. E. (2021). Multimedia learning (3rd ed.). Cambridge University Press. [Google Scholar] [Crossref]

38. Mendoza, J. L., & Garcia, P. T. (2022). Validation of localized e-learning materials for junior high school science in the Philippines. International Journal of Instruction, 17(1), 205–222. [Google Scholar] [Crossref]

39. Mengesha, Z., Heldreth, C., Lahav, M., Sublewski, J., & Tuennerman, E. (2021). "I don't think these devices are very culturally sensitive"—Impact of automated speech recognition errors on African Americans. Frontiers in Artificial Intelligence, 4, Article 725911. [Google Scholar] [Crossref]

40. OECD. (2023). PISA 2022 results (Volume I): The state of learning and equity in education. OECD Publishing. https://doi.org/10.1787/53f23881-en [Google Scholar] [Crossref]

41. Ormancı, Ü. (2020). The effect of digital inquiry-based learning on students' academic achievement and attitudes: A meta-analysis. Education and Information Technologies, 25, 4545–4565. [Google Scholar] [Crossref]

42. https://doi.org/10.1007/s10639-020-10178-x [Google Scholar] [Crossref]

43. Ormancı, Ü. (2025). Effects of web-assisted guided inquiry activities on students' systems thinking skills in science education. Journal of Science Education and Technology, 34(2), 189–204. [Google Scholar] [Crossref]

44. https://doi.org/10.1007/s10956-025-10238-9 [Google Scholar] [Crossref]

45. Pardo, F., Cánovas, Ó., & Clemente, F. J. G. (2025). Audio features in education: A systematic review of computational applications and research gaps. Applied Sciences, 15(12), 6911. [Google Scholar] [Crossref]

46. https://doi.org/10.3390/app15126911 [Google Scholar] [Crossref]

47. Polanin, J. R., Austin, M., Taylor, J. A., Steingut, R. R., Rodgers, M. A., & Williams, R. (2024). Effects of the 5E instructional model: A systematic review and meta-analysis. Review of Educational Research. https://doi.org/10.3102/00346543241234567 [Google Scholar] [Crossref]

48. Prabhu, D., Jyothi, P., Ganapathy, S., & Unni, V. (2023). Accented speech recognition with accent-specific codebooks. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (pp. 7175–7188). Association for Computational Linguistics. [Google Scholar] [Crossref]

49. https://doi.org/10.18653/v1/2023 [Google Scholar] [Crossref]

50. Reyes, A. (2022). Inquiry-based strategies in teaching chemical bonding: Effects on student understanding and engagement. Philippine Journal of Science Teaching, 16(1), 88–103. [Google Scholar] [Crossref]

51. Sáiz-Manzanares, M. C., Marticorena-Sánchez, R., & Ochoa-Orihuel, J. (2020). Effectiveness of using voice assistants in learning: A study at the time of COVID-19. International Journal of Environmental Research and Public Health, 17(15), 5618. https://doi.org/10.3390/ijerph17155618 [Google Scholar] [Crossref]

52. Sam, R. (2024). Systematic review of inquiry-based learning: Assessing impact and best practices in education. F1000Research, 13, 1045. https://doi.org/10.12688/f1000research.155367.1 [Google Scholar] [Crossref]

53. Santos, R., & Magno, E. (2020). Interactive voice applications as inquiry support tools in Physics and Chemistry. Philippine Educational Research Journal, 18(2), 44–62. [Google Scholar] [Crossref]

54. Tabuena, A. K. G. (2025). Practices of instructors in integrating artificial intelligence (AI) in assessing student performance among community colleges in Albay. International Journal of Research and Innovation in Social Science, 9(3), 333–342. [Google Scholar] [Crossref]

55. Trust, T., & Whalen, J. (2020). Should teachers be trained in emergency remote teaching? Lessons learned from the COVID-19 pandemic. Journal of Technology and Teacher Education, 28(2), 189–199. [Google Scholar] [Crossref]

56. UNESCO. (2023). Technology in education: A tool on whose terms? Global Education Monitoring Report 2023. UNESCO Publishing. https://doi.org/10.54675/PCSD6738 [Google Scholar] [Crossref]

57. U.S. Department of Education. (2023). Artificial intelligence and the future of teaching and learning: Insights and recommendations. Office of Educational Technology. https://www.ed.gov [Google Scholar] [Crossref]

58. Villanueva, J. (2021). Effectiveness of voice-assisted digital modules in Science education. Philippine Journal of Educational Innovations, 10(3), 77–89. [Google Scholar] [Crossref]

59. Vincent, D. A., Okeowo, R. O., & Ariyo, S. O. (2024). The use of assistive technology for students with disabilities in technical colleges in Ondo State. Journal of Educational Research and Practice, 14(1), 53–66. https://doi.org/10.5590/jerap.2024.14.1.04 [Google Scholar] [Crossref]

60. Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press. [Google Scholar] [Crossref]

61. Weymuth, T., & Reiher, M. (2020). Immersive interactive quantum mechanics for teaching and learning chemistry. arXiv Preprint. https://arxiv.org/abs/2011.03256 [Google Scholar] [Crossref]

62. Yánez-Pérez, C. L., Vargas-Díaz, Y., & Esparza-Rodríguez, R. (2024). Design and validation of IndagApp: A mobile application to support inquiry-based science teaching. Education and Information Technologies, 29(5), 11793–11812. https://doi.org/10.1007/s10639-024-12556-4 [Google Scholar] [Crossref]

63. Zhang, L., & Lin, C. (2020). Interactive voice assistants in Chemistry education: A quasi-experimental study. Educational Technology Research and Development, 68(5), 2431–2450. [Google Scholar] [Crossref]

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