Pages: 39

No chapter abstract available.

Pages: 20

This chapter explores blended learning as a transformative approach to language education, combining theoretical insights with practical strategies. It emphasizes the fusion of face-to-face and digital instruction, guided by frameworks like CoI, TPACK, and SAMR. Topics include synchronous and asynchronous delivery, mobile learning, microlearning, AI integration, student engagement, and digital assessment. Aimed at educators, trainers, curriculum designers, and policymakers, it offers actionable guidance on curriculum design and institutional readiness—encouraging thoughtful innovation in tech-enhanced language teaching.

Pages: 19

Digital technologies in language education offer expanded access but risk reinforcing linguistic inequities through algorithmic bias. This chapter critiques AI tools like Grammarly and Duolingo for privileging dominant languages (e.g., English) while marginalizing dialects, creoles, and non-native accents. Using linguistic human rights and algorithmic critique, it examines cases such as Vietnamese tonal penalties, Caribbean creole erasure, and Indigenous language exclusion—tracing bias to homogenized data, monocultural design, and commercial standardization. To promote equity, it proposes three strategies: (1) technical fixes like adversarial debiasing and participatory design; (2) pedagogical shifts including translanguaging and teacher-AI mediation; and (3) policy reforms such as the EU AI Act and data sovereignty laws. The chapter calls for urgent, cross-disciplinary collaboration to ensure technology supports, rather than suppresses, linguistic diversity.

Pages: 59

No chapter abstract available.

Pages: 16

Digital Game-Based Language Learning (DGBLL) is reshaping language education by embedding interactive gaming elements into instruction. This chapter outlines its pedagogical foundations, showing how storytelling, adaptive learning, and problem-solving boost vocabulary, grammar, and communication skills. It evaluates various game types—serious games, simulations, and multiplayer platforms—for their effectiveness, and offers strategies for aligning DGBLL with curriculum, tech needs, and assessment. While DGBLL enhances motivation, personalization, and feedback, it also poses challenges like teacher training, digital distractions, and assessment integration. Case studies and research highlight its positive impact on engagement and proficiency. The chapter also explores policy and curriculum implications, offering best practices to help educators create dynamic, student-centered environments that strengthen both language and digital literacy.

Pages: 17

The global business landscape demands industry-specific communication skills, yet traditional Business English courses often lack contextual relevance. This chapter explores how AI-driven adaptive learning systems—leveraging machine learning and natural language processing—customize content to professional roles like marketing or finance, offering real-time feedback, simulations, and culturally aware interactions. It reviews research on AI tools that enhance engagement by tailoring tone, context, and vocabulary to workplace challenges, especially within blended learning environments. The chapter also examines the use of Large Language Models (LLMs) for hyper-contextual learning and addresses ethical concerns like bias reduction and explainability. With evidence of improved outcomes, it presents practical strategies for educators and offers a transformative framework for modernizing Business English education.

Pages: 12

Artificial intelligence (AI), a cornerstone of modern innovation, uses machine learning and deep learning to enable machines to perform tasks like learning, reasoning, and decision-making. In language education, AI is transforming rigid curricula into dynamic, inclusive platforms by offering personalized learning paths, multilingual support, and culturally responsive content. This chapter examines how AI-driven systems promote equitable language learning, while highlighting a research gap in balancing inclusivity with pedagogical quality and engagement. Grounded in digital pedagogy and inclusive education theory, it uses qualitative analysis of AI platforms and educator interviews to explore how adaptive technologies meet diverse learner needs. It also addresses critical challenges, including algorithmic bias, digital accessibility, and maintaining learner autonomy. By evaluating both the promise and limitations of AI in inclusive language education, the chapter contributes to shaping ethical, human-centered systems that foster diversity, equity, and global communication.

Pages: 14

Language acquisition theories—nativist, behaviorist, and interactionist—all highlight the environment’s role in shaping linguistic competence and identity. This environment can be physical or virtual, with social media offering rich exposure to diverse languages and cultures. Through formats like videos, podcasts, quizzes, and games, social media supports linguistic, cultural, and generational growth, influencing all four core language skills. It also facilitates Task-Based Language Learning (TBLL), engaging learners in real-world tasks that build communicative ability. This chapter explores how social media fosters language development, identity formation, skill enhancement, and feedback-driven learning.

Pages: 89

No chapter abstract available.

Pages: 21

Cultural and linguistic theories have long held that human language cognition is innate (Chomsky’s LAD). In contrast, digital tools like ChatGPT, Duolingo, and Babbel rely on data-driven algorithms and pattern recognition. This study explores whether such AI tools can imitate, support, or enhance human cognition, highlighting their limitations in replicating innate linguistic abilities. While AI excels in grammar correction and personalized phrasing, it lacks semantic depth, cultural sensitivity, and emotional nuance. Using mixed methods, the research concludes that AI tools can enrich—but not replace—human language learning, advocating for AI-supported pedagogy in modern linguistic education.

Pages: 27

This study evaluated the effectiveness of popular language learning apps in the U.S., focusing on their impact on linguistic proficiency and cultural exchange. Using surveys and interviews with 200 migrants and 20 frequent users, the research found Duolingo to be the most preferred platform among university students. Its adaptive learning, instant feedback, gamification, and varied exercises enhanced vocabulary, grammar, and cultural awareness. Students who used the app longer showed greater language improvement, while interactive exercises were less impactful. Duolingo’s community features—forums, clubs, and peer interactions—also supported intercultural communication, making it a widely recommended tool across age groups.

Pages: 11

This chapter addresses the digital divide’s impact on preserving endangered languages, focusing on the Irula tribe in India’s Western Ghats. It explores how digital storytelling, integrated into a Mobile-Assisted Language Learning (MALL) framework, can support Irula language revitalization. Drawing on situated learning and social constructivist theories, the study outlines methods for embedding indigenous knowledge—such as oral history and folklore—into mobile platforms. Using mixed research methods, it assesses how culturally tailored digital narratives enhance language skills, motivation, and cultural identity. The chapter also highlights challenges like low digital literacy and tech access, proposing community-driven solutions and policy recommendations to scale this innovative approach.

Pages: 14

Artificial intelligence is reshaping digital language instruction and assessment through tools like intelligent tutoring systems, machine translation, voice recognition, and adaptive platforms—enhancing learning outcomes and teaching efficiency. This paper offers a theoretical and analytical review of AI’s integration into language education, examining its benefits, challenges, and broader implications. It highlights key frameworks, methodological concerns, and the ethical, educational, and sociocultural dimensions of AI use. Focusing on low-resource Indian languages, Gondi and Khasi, the study explores AI’s potential to support Indigenous language learning. Drawing on sociocultural theory and critiques of algorithmic bias, it argues for AI as a complement to human teaching—amplifying Indigenous knowledge rather than replacing it. The paper advocates for community-driven, culturally sensitive AI design over top-down approaches, emphasizing co-creation and equity in digital language education.

Pages: 16

This study investigates how AI platforms support speaking skill development among ESL learners in Vietnam, using quantitative data from an online survey of 63 participants. Variables such as age, academic background, and English proficiency were considered. Findings show that while frequent AI use alone doesn’t guarantee improved outcomes, learners’ perception of AI’s usefulness strongly correlates with language development and satisfaction. Extended engagement with AI tools led to noticeable gains in speaking proficiency, especially when adaptive feedback was effective. However, prolonged use also led to reduced interest for some learners due to repetitive exercises causing boredom and fatigue. As the study is cross-sectional, future longitudinal research is recommended to assess long-term impacts and identify barriers to sustained AI use in speaking practice.

Pages: 28

No chapter abstract available.

Pages: 15

Technology has long played a key role in English language education, and recent shifts toward holistic, student-centered, and competency-based assessment have enhanced the validity and authenticity of evaluations. Digital tools have transformed both formative and summative assessments, making them more engaging and personalized through real-world tasks and content-driven activities. Innovations such as LMS, CBT, AI-powered assessments, MALT, automated writing/speaking tools, gamification, and VR cater to diverse learner needs and enable more accurate, adaptive evaluations. These tools support balanced learning by improving feedback, promoting autonomy, and streamlining testing. However, challenges remain—technical limitations and pedagogical concerns can impact effectiveness. A hybrid approach that blends technology with human insight is essential. This chapter critically examines emerging trends, tools, and challenges, offering guidance for educators, researchers, and policymakers. A blended model ensures innovation while preserving fairness, inclusivity, and educational integrity.

Pages: 13

Social media has reshaped language education by fostering interaction, autonomy, and engagement beyond the classroom. Platforms like YouTube, Instagram, Facebook, and WhatsApp are widely used, yet their pedagogical impact remains underexplored. This chapter investigates social media as a tech-mediated language learning tool through a mixed-methods approach involving surveys and interviews with learners and educators. It highlights social media’s potential to boost motivation, offer authentic exposure, and support collaboration, while addressing challenges like digital fatigue, access inequality, and privacy concerns. Grounded in digital pedagogy, the chapter provides practical strategies to bridge theory and practice in tech-enhanced language learning.