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The Impact of AI-Enhanced Digital Textbooks on University Students’ Academic Performance in China.

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Title: The Impact of AI-Enhanced Digital Textbooks on University Students’ Academic Performance in China.
Alternate Title: El impacto de los libros de texto digitales mejorados con Inteligencia Artificial en el rendimiento académico de los Estudiantes Universitarios en China.
Authors: Li, Yong1 liyong@cqtbi.edu.cn
Source: International Journal of Educational Research & Innovation (IJERI). 2026, Issue 25, p1-18. 18p.
Abstract (English): This research explores the mediating mechanisms and contingent factors that underlie the effectiveness of AI-enhanced digital textbooks (AI-EDT) for Chinese university students. Using partial least squares structural equation modeling (PLSSEM) on data from 554 students, this paper proposes a conceptual framework where AI textbook features (AITF) affect academic performance (AP) via two distinct pathways: perceived learning impact (PLI) and self-directed learning (SDL). The results indicate that AITF exerts a positive effect on PLI (β = 0.427) and SDL (β = 0.356), which partially mediate the AITF-AP relationship. Learning infrastructure and support (LIS) moderates the AITF-PLI link (β = 0.189), while comparative effectiveness (CE) reinforces the PLI-AP connection (β= 0.234). This shows that AI-EDT’s success is not merely technological but contingent upon robust institutional ecosystems and learners’ favorable comparative perceptions. It provides an evidence-based framework for integrating AI-driven tools and highlights the synergy of pedagogical innovation, behavioral adaptation, and contextual support. [ABSTRACT FROM AUTHOR]
Abstract (Spanish): Esta investigación analiza los mecanismos mediadores y los factores contingentes que subyacen a la efectividad de los libros de texto digitales mejorados con inteligencia artificial (AI-EDT) en estudiantes universitarios chinos. Empleando modelado de ecuaciones estructurales por mínimos cuadrados parciales (PLS-SEM) con datos de 554 estudiantes, este artículo propone un marco conceptual en el que las características de los libros de texto con IA (AITF) inciden en el rendimiento académico (AP) mediante dos vías diferenciadas: el impacto percibido en el aprendizaje (PLI) y el aprendizaje autodirigido (SDL). Los resultados revelan que las AITF ejercen un efecto positivo sobre el PLI (β = 0.427) y el SDL (β = 0.356), los cuales median parcialmente la relación AITF-AP. La infraestructura y el soporte para el aprendizaje (LIS) moderan el vínculo AITF-PLI (β = 0.189), en tanto que la efectividad comparativa (CE) fortalece la conexión PLI-AP (β = 0.234). Estos hallazgos demuestran que el éxito de los AI-EDT no radica únicamente en su dimensión tecnológica, sino que depende de ecosistemas institucionales y de percepciones comparativas favorables por parte de los estudiantes. El estudio ofrece un marco fundamentado en evidencia para la integración de herramientas basadas en IA, al tiempo que subraya la sinergia entre innovación pedagógica, adaptación conductual y soporte contextual. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Educational Research & Innovation (IJERI) is the property of Revista IJERI - Universidad Pablo de Olavide and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
FullText Text:
  Availability: 0
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DbLabel: Education Research Complete
An: 195448118
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Label: Title
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  Data: The Impact of AI-Enhanced Digital Textbooks on University Students’ Academic Performance in China.
– Name: TitleAlt
  Label: Alternate Title
  Group: TiAlt
  Data: El impacto de los libros de texto digitales mejorados con Inteligencia Artificial en el rendimiento académico de los Estudiantes Universitarios en China.
– Name: Author
  Label: Authors
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  Data: <searchLink fieldCode="AR" term="%22Li%2C+Yong%22">Li, Yong</searchLink><relatesTo>1</relatesTo><i> liyong@cqtbi.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Educational+Research+%26+Innovation+%28IJERI%29%22">International Journal of Educational Research & Innovation (IJERI)</searchLink>. 2026, Issue 25, p1-18. 18p.
– Name: Abstract
  Label: Abstract (English)
  Group: Ab
  Data: This research explores the mediating mechanisms and contingent factors that underlie the effectiveness of AI-enhanced digital textbooks (AI-EDT) for Chinese university students. Using partial least squares structural equation modeling (PLSSEM) on data from 554 students, this paper proposes a conceptual framework where AI textbook features (AITF) affect academic performance (AP) via two distinct pathways: perceived learning impact (PLI) and self-directed learning (SDL). The results indicate that AITF exerts a positive effect on PLI (β = 0.427) and SDL (β = 0.356), which partially mediate the AITF-AP relationship. Learning infrastructure and support (LIS) moderates the AITF-PLI link (β = 0.189), while comparative effectiveness (CE) reinforces the PLI-AP connection (β= 0.234). This shows that AI-EDT’s success is not merely technological but contingent upon robust institutional ecosystems and learners’ favorable comparative perceptions. It provides an evidence-based framework for integrating AI-driven tools and highlights the synergy of pedagogical innovation, behavioral adaptation, and contextual support. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label: Abstract (Spanish)
  Group: Ab
  Data: Esta investigación analiza los mecanismos mediadores y los factores contingentes que subyacen a la efectividad de los libros de texto digitales mejorados con inteligencia artificial (AI-EDT) en estudiantes universitarios chinos. Empleando modelado de ecuaciones estructurales por mínimos cuadrados parciales (PLS-SEM) con datos de 554 estudiantes, este artículo propone un marco conceptual en el que las características de los libros de texto con IA (AITF) inciden en el rendimiento académico (AP) mediante dos vías diferenciadas: el impacto percibido en el aprendizaje (PLI) y el aprendizaje autodirigido (SDL). Los resultados revelan que las AITF ejercen un efecto positivo sobre el PLI (β = 0.427) y el SDL (β = 0.356), los cuales median parcialmente la relación AITF-AP. La infraestructura y el soporte para el aprendizaje (LIS) moderan el vínculo AITF-PLI (β = 0.189), en tanto que la efectividad comparativa (CE) fortalece la conexión PLI-AP (β = 0.234). Estos hallazgos demuestran que el éxito de los AI-EDT no radica únicamente en su dimensión tecnológica, sino que depende de ecosistemas institucionales y de percepciones comparativas favorables por parte de los estudiantes. El estudio ofrece un marco fundamentado en evidencia para la integración de herramientas basadas en IA, al tiempo que subraya la sinergia entre innovación pedagógica, adaptación conductual y soporte contextual. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Educational Research & Innovation (IJERI) is the property of Revista IJERI - Universidad Pablo de Olavide and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.46661/ijeri.129
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            – D: 01
              M: 01
              Text: 2026
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              Y: 2026
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