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Enhancing Second Language Reading through CALL: A Literature Review on Navigating Socio-Cultural and Technological Pathways
Dive into our latest exploration of Second Language Reading through CALL. Discover how innovative technology not only overcomes traditional educational barriers but also opens up new pathways for understanding and engagement in the realm of language learning. From socio-cultural dynamics to the latest technological advancements, learn how CALL is reshaping the way we approach second language reading.
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Last updated
Last updated October 19, 2026: a plain-English reader’s guide, Short Version, and table of contents added, the review’s section titles promoted to real headings so the contents list works, three images unwrapped from an unverifiable short link and given specific descriptions, a duplicated image and one dead reference link removed, a 2026 update on AI-assisted reading instruction, a FAQ, reader poll, and Bottom Line added. The original literature review is kept intact below.
Table of Contents
Start Here: Reading Is Where the Quiet Students Win
Of the four language skills, reading is the one a student can practice alone, at midnight, with no partner and no audience, which is exactly why technology took to it first. Computer-assisted language learning, CALL for short, built its earliest reading tools around a humble idea: put the help inside the text, at the exact word where the reader gets stuck. This literature review gathers what the research says about those tools: the glosses, the collaborative platforms, the mobile apps, and where they fall short.
I taught Spanish for 11 years, and reading was where my quietest students quietly lapped the class talkers. The review below explains the machinery behind that, and the 2026 update near the end covers what happened when AI chatbots joined the reading lesson. It pairs with the description of the Japanese writing system and the companion literature review on ChatGPT and classroom outcomes.
The Short Version
- Glossing works. Taylor’s 2021 meta-analysis found that built-in explanations of hard words significantly improve second language reading comprehension, with the type of gloss matched to the learner.
- CALL works better socially than solo. Wang and Liao (2017) found reading gains grew when CALL was combined with collaborative learning, not just individual screen time.
- Generation and culture shape uptake: digital natives and analog readers want different formats, and open platforms can build cross-cultural community around reading (Dani, 2023; Mikroyannidis et al., 2023).
- The limits are real: tools still simulate natural language use poorly, and specialized fields need specialized vocabulary support, so CALL supplements teaching rather than replacing it.
Contents
- Start Here: Reading Is Where the Quiet Students Win
- Introduction
- Demonstrated Effectiveness of CALL for L2 Reading
- Socio-Cultural Considerations in CALL
- Where Things Stand in 2026
- Frequently Asked Questions
- References
Professor Winston Wants to Know
Our resident watchdog is taking the temperature of the room.
When you read in a second language, what helps you most?
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Want to say more? The comments below are open.
Introduction
In the contemporary global landscape, proficiency in reading a second language (L2) transcends academic achievement, serving as a bridge to diverse cultures, knowledge, and opportunities. The advent of Computer-Assisted Language Learning (CALL) marks a pivotal shift in language education, presenting dynamic, interactive tools that extend beyond the conventional classroom’s confines. While the potential of CALL to bolster L2 reading is considerable, unlocking this potential necessitates a nuanced comprehension of both its capabilities and the inherent challenges of integrating technology within the intricate process of language learning.
This literature review embarks on a critical examination of Second Language Reading through CALL. CALL’s utility in surmounting the specific hurdles associated with L2 reading. It scrutinizes empirical research on the efficacy of diverse CALL tools and methodologies, considering how their adoption is shaped by technological, pedagogical, and socio-cultural factors. Aimed at offering a layered understanding of CALL, this review dissects its strengths and weaknesses, providing insights into optimizing its application in language instruction.
Focusing on second language reading, a cornerstone skill for academic excellence and global communication, this review endeavors to shed light on evidence-based strategies for CALL’s effective deployment. Moreover, it highlights the persisting research questions critical to advancing the CALL domain. Ultimately, this review seeks to enrich the discourse on technology-assisted language learning, advocating for the development of effective, inclusive CALL practices in second language reading instruction.
Demonstrated Effectiveness of CALL for L2 Reading
The impact of Computer-Assisted Language Learning (CALL) on enhancing second language (L2) reading proficiency is well-documented. A quintessential example is CALL glossing, which, as Taylor (2021) reveals in a meta-analysis, significantly bolsters L2 reading comprehension. This study not only attests to glossing’s efficacy but also accentuates the necessity of tailoring gloss types (textual vs. pictorial) to learners’ needs, a testament to CALL’s potential to cater to the diverse preferences of L2 learners. For instance, Taylor found that textual glosses, when used in conjunction with questions assessing comprehension, led to a notable improvement in understanding complex texts among intermediate learners.
Further evidence of CALL’s potency emerges from Wang and Liao’s (2017) investigation into its integration with collaborative learning strategies. Their research, which demonstrated notable advancements in language skills, including L2 reading proficiency, when CALL was merged with innovative pedagogical methods, underscores the symbiotic relationship between technology and pedagogy. This alignment with Vygotsky’s socio-cultural theory highlights how social interaction, mediated by digital tools, can enhance cognitive development and language acquisition.
Socio-Cultural Considerations in CALL
Integrating CALL into L2 reading instruction demands an acute awareness of the socio-cultural barriers impacting its acceptance. Dani (2023) explores the generational divide in reading preferences, revealing a stark contrast between digital-native Generation Z and analog-preferring Generation X. This dichotomy suggests that a uniform approach to CALL may alienate certain learner demographics, emphasizing the need for adaptable and versatile CALL solutions.
Moreover, the essence of community and inclusivity in language learning is poignantly captured by Mikroyannidis, Perifanou, and Economides (2023), who illustrate how an open-source language learning platform can bridge cultural divides and foster cross-cultural understanding. Their initiative resonates with the principles of sustainable design, illustrating CALL’s capacity to democratize language learning and ensure its long-term viability and relevance.
Technological Challenges and Innovations
Understanding the technological constraints and ongoing innovations is paramount for the fruitful implementation of CALL in L2 reading. Saba (2022) critiques the limitations of CALL, notably its struggle to mimic the intricacies of natural language learning environments. This critique points to a significant gap in CALL development, urging for tools that facilitate not just mechanical interaction but also authentic, meaningful language use.
Dimulescu and Nechifor (2022) underscore the importance of mobile learning in making language instruction more accessible and tailored, particularly for English for Specific Purposes (ESP). This aligns with emerging needs for flexibility in learning, enabling students to engage with language content that directly pertains to their field of study or interest.
Where CALL Falls Short: Limitations & Critiques
Despite the optimistic horizon that CALL presents, its limitations, particularly in simulating the complex interplay of natural language learning, remain a pertinent critique. As Saba (2022) points out, the technological reproduction of linguistic subtleties and social interactions inherent in language acquisition poses a considerable challenge. This limitation is notably impactful in the realm of L2 reading, where depth of comprehension and fluency hinges on engaging with the language in its natural, unstructured form. Recognizing and addressing these limitations are critical for educators and developers alike to navigate effectively, ensuring CALL’s strengths are maximized while its shortcomings are mitigated through complementary pedagogical strategies.
Additionally, the specialization required for effective English for Specific Purposes (ESP) instruction, as discussed by Dimulescu and Nechifor (2022), illustrates another dimension of CALL’s limitations. The demand for CALL tools to adapt to the specialized lexicon and discourse patterns of various fields underscores the need for customizable and flexible CALL solutions, tailored to meet the specific needs of diverse learner populations.
Assessing the Impact and Choosing CALL Approaches
The effectiveness of Computer-Assisted Language Learning (CALL) in second language (L2) reading has emerged from a robust body of research, underscoring CALL’s dynamic potential to facilitate significant learning gains. Studies such as those conducted by Taylor (2021) and Wang and Liao (2017) have illuminated the nuanced ways in which CALL can enhance reading comprehension and fluency, presenting a compelling case for its integration into language learning curricula. These findings are rooted in a blend of cognitive and sociocultural theoretical frameworks, suggesting that CALL’s multimedia tools can support multiple channels of information processing, while its capacity for authentic social interaction aids in the application of language in real-world contexts.
Despite the optimistic landscape painted by current research in second language reading through CALL, gaps remain that challenge the comprehensive adoption of CALL. The variability in CALL’s effectiveness across different learner demographics, highlighted by contrasting preferences between digital and analog reading modalities, suggests an area ripe for deeper exploration. Moreover, the question of how different CALL tools affect long-term learner engagement and motivation in L2 reading remains only partially answered, indicating a crucial direction for future research endeavors.
As the technological landscape continues to evolve, so too does the interface of CALL with language learning, beckoning a continuous assessment of its methodologies against emerging educational needs. The promise of CALL in fostering not only linguistic competencies but also cultural awareness and global connectivity underscores the necessity of further investigative work. This future research must pivot towards elucidating the long-term impacts of CALL on L2 reading proficiency, and how personalization and adaptability of CALL resources can meet the diverse needs of an increasingly global learner population.
In navigating the selection of CALL approaches, the synthesis of empirical evidence with practical educational considerations becomes paramount. The balance between theoretical robustness and empirical support against the backdrop of accessibility and pedagogical relevance will guide the effective implementation of CALL strategies. This strategic selection is driven by the imperative to not only harness the benefits of current CALL technologies but also to anticipate the evolving landscape of language education in a technologically integrated world.
Through a meticulous analysis of existing research and the identification of pivotal areas for further inquiry, this literature review contributes to a foundational understanding of CALL’s role in L2 reading. The ongoing dialogue between current knowledge and the anticipation of future technological and pedagogical advancements encapsulates the dynamic challenge of optimizing CALL’s impact on language learning. As educators, researchers, and technologists collaborate, the exploration of CALL in L2 reading continues to unfold, promising avenues for enriching language education in our increasingly interconnected world.
Where Things Stand in 2026
This review was written just before AI chatbots walked into the reading lesson, and the first careful classroom comparisons have now arrived. A 2026 quasi-experimental study in Frontiers in Education followed 60 university EFL students in Jordan across a semester, in three sections: one where the teacher used ChatGPT as a managed scaffold for previewing, predicting, monitoring, questioning, inferencing, and summarizing; one taught the same strategies teacher-led; and one business-as-usual section with no explicit strategy training. Both strategy sections outperformed business as usual, and the teacher-managed AI section posted the highest adjusted reading scores and the largest gains in metacognitive awareness. The authors are careful about the design, one intact section per condition, and their conclusion matches this review’s: AI extends explicit strategy instruction, it does not displace teacher judgment.
The student side of the screen is changing too. A 2026 qualitative study of postgraduate second language readers using ChatGPT for academic reading (Frontiers in Psychology, doi:10.3389/fpsyg.2026.1787647) found learners planning, monitoring, and evaluating as earlier reading research would predict, plus a strategy the print era never needed: debugging, catching the AI’s errors and refining their prompts until the help was actually helpful. Stronger readers verified more. That is the review’s old warning about uncritical acceptance, now with a named counter-skill.
Put together, the 2026 evidence sharpens rather than overturns what follows. The active ingredient in CALL reading was never the device. It was always the strategy instruction and the help delivered at the moment of need. AI makes both cheaper to deliver at scale, provided a teacher stays in charge of the lesson.
Conclusion
This literature review has traversed the multifaceted domain of Computer-Assisted Language Learning (CALL), illuminating its significant potential to redefine second language reading instruction. However, it also brings to light the myriad challenges and considerations that accompany the adoption of CALL, from technological constraints and socio-cultural barriers to the critical need for pedagogical alignment.
To move forward, future research must concentrate on bridging the gap between CALL’s current capabilities and the authentic, interactive experiences essential for robust L2 reading development. This includes addressing the technological limitations that hinder the simulation of natural language learning environments and enhancing the adaptability of CALL tools to meet the diverse needs of learners.
Moreover, the development of CALL applications should prioritize inclusivity, aiming to create accessible, user-friendly platforms that support a wide range of learning styles and preferences. By fostering a collaborative effort among educators, researchers, and technology developers, the field of CALL can continue to evolve, offering more effective and engaging learning experiences for second language readers.
In essence, this review advocates for a balanced and critical approach to the integration of CALL in L2 reading instruction, one that recognizes its vast potential while also acknowledging the complexities of effective implementation. Through continuous innovation, rigorous evaluation, and a commitment to inclusivity, the promise of CALL to enhance second language reading skills can be fully realized, paving the way for a new generation of proficient and confident L2 readers.
Frequently Asked Questions
What is CALL, in one sentence?
CALL is computer-assisted language learning: using digital tools, from glossed e-texts to AI chatbots, to help people learn another language. This review covers CALL applied to one skill, reading in a second language.
What is glossing, and why does it help reading?
A gloss is a short explanation of a hard word or phrase, built into the text where the reader meets it: a definition, a translation, sometimes a picture. Taylor’s 2021 meta-analysis found glossing significantly improves comprehension, because the reader gets help at the moment of need instead of losing the thread on a trip to the dictionary.
Does reading on a screen hurt second language reading?
Not by itself. Dani (2023) found generational differences in format preference, with younger readers at home on screens and older readers preferring print, which argues for offering both. What the research consistently supports is scaffolding, glosses, strategy prompts, and level-appropriate texts, in whichever format the learner will actually finish.
Can an AI chatbot teach reading strategies?
As a teacher-managed scaffold, the early evidence is promising: the 2026 Frontiers in Education study found the highest reading scores in the section where ChatGPT prompted strategies like predicting and summarizing under a teacher’s direction. Evidence for chatbots as standalone reading teachers, with no teacher managing the lesson, is much thinner.
How should a teacher choose a CALL reading tool?
Check four things: whether the glosses and texts match your students’ level, whether the tool prompts real strategies (predict, question, summarize) instead of just displaying text, whether its content ranges across cultures rather than one textbook world, and whether student data stays private. Then pilot it on one unit before committing a semester.
The Bottom Line
Second language reading through CALL works when it does three things: help at the word level the instant it is needed, strategies taught explicitly rather than assumed, and people, classmates or a teacher, somewhere in the loop. The 2026 AI studies strengthen that verdict instead of changing it. Buy the tool that does those three things and skip the one that only promises engagement.
One question for the road: when you read in another language, what do you reach for first, the dictionary, the AI, or the margin of the page? In twenty-four years of classrooms I watched the margin of the page win more often than any software. Tell me your habit in the comments.
References
Coy, A., Mohammed, P. S., Hosein, A., Skerrit, P., Mohammed, A., & Lewis-Fokum, Y. (2021). Increasing the integration of computer-assisted language learning in Caribbean schools: Lessons from the use of an intelligent spelling tutor during COVID-19. Journal of Eastern Caribbean Studies, 46(1 and 2), 60-90.
Dani, E. (2023). Analogous versus digital reading: A comparative study. Acta Universitatis Sapientiae, Philologica, 15(1), 177–191. https://doi.org/10.2478/ausp-2023-0012
Dimulescu, C., & Nechifor, A. (2022). Accommodating English for specific purposes to computer assisted language learning. Bulletin of the Transilvania University of Brasov. Series IV: Philology and Cultural Studies, 14(63). https://doi.org/10.31926/but.pcs.2021.63.14.2.1
Mikroyannidis, A., Perifanou, M., & Economides, A. A. (2023). Developing a sustainable online platform for language learning across Europe. Computers, 12(7), 140. https://doi.org/10.3390/computers12070140
Saba, N. (2022). A critical analysis of computer assisted language learning (CALL) in particular relation to the learning of speaking skills of a foreign language. Journal of Pharmaceutical Negative Results, 13(Special Issue 7), 5285-5289.
Taylor, A. M. (2021). Technology and reading: The effects of CALL glossing. Psychological Reports, 124(5), 2092–2118. https://doi.org/10.1177/0033294120954139
Wang, Y.-h., & Liao, H.-C. (2017). Learning performance enhancement using computer-assisted language learning by collaborative learning groups. Symmetry, 9(8), 141. https://doi.org/10.3390/sym9080141
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