Special Issue Proposal
"AI-mediated Informal Digital Learning of English (AIIDLE): Insights, Challenges, and Practice"
Corresponding Guest Editor:
Dr. Ehsan Namaziandost (PhD in Applied Linguistics (TEFL); Department of English, Ahvaz Branch, Islamic, Azad University, Ahvaz, Iran (e.namazi75@yahoo.com; e.namazi75@gmail.com)
https://scholar.google.com/citations?user=lQh-tNkAAAAJ&hl=en
Guest Editor:
Dr. Fidel Çakmak
Associate Professor, Department of Foreign Language Education, Alanya Alaaddin Keykubat University, Turkey
https://scholar.google.com/citations?user=BWjB6loAAAAJ&hl=en
fidel.cakmak@yahoo.com; fidel.cakmak@alanya.edu.tr
Aim and Scope
With the rise of artificial intelligence (AI), there has been an unprecedented transformation in how learners access and interact with English outside traditional classrooms. This proposed special issue on AI-mediated Informal Digital Learning of English (AI-IDLE): Insights, Challenges, and Practice aims to explore the role of AI in shaping informal English learning through digital platforms. As AI-driven technologies like chatbots, recommendation algorithms, adaptive learning apps, and virtual tutors become integral to language learning, there is a pressing need to understand their impact on informal learning environments, assess associated challenges, and share best practices.
This special issue seeks to gather research that provides comprehensive insights into the potential of AI to enhance English learning in informal, self-directed contexts. It will focus on the opportunities AI offers for personalized, accessible, and interactive learning experiences while also addressing the complexities, ethical concerns, and pedagogical implications that arise in this emerging field.
Key Themes
This issue will invite contributions across several interconnected themes, including but not limited to:
1) AI-enabled Insights and Innovations in Informal Learning
•Exploring the role of AI-driven tools and platforms (e.g., language learning apps, social
media, virtual companions) in informal English learning.
•Personalization and adaptive learning experiences: How AI customizes content to fit
individual learner needs and preferences.
•Analysis of learning patterns and insights from data on learner behaviors, engagement, and
outcomes.
2) Challenges and Ethical Considerations in AI-IDLE
•Accessibility and Equity: Addressing the digital divide and ensuring that AI-mediated
learning is accessible to diverse learner populations.
•Bias and Cultural Relevance: Examining the cultural implications of AI language models,
including the impact of biases on learning experiences and content delivery.
•Privacy and Data Security: Understanding privacy concerns related to data collection, AI
transparency, and ethical considerations in learner data usage.
3) Applications and Best Practices for AI in Informal English Learning
•Case studies of successful AI-mediated English learning across diverse cultural and social
contexts.
•Innovative instructional design and strategies that integrate AI for informal learning.
•Redefining teacher and facilitator roles in AI-mediated environments: How educators can
complement and support AI-driven language learning.
4) Evaluation and Assessment of AI-Mediated Learning Outcomes
•Metrics for assessing language proficiency and other learning outcomes in informal, AI-driven contexts.
•Evaluating the long-term impact of AI-mediated informal learning on language acquisition.
•Learner perspectives on AI tools: Motivation, engagement, and perceived efficacy.
5) Cognitive and Linguistic Development in AI-mediated Learning
•Language Acquisition and Development: How does informal AI-mediated learning
influence vocabulary growth, grammar acquisition, and pronunciation in English learners?
•Cognitive Load and Processing: What cognitive processes are involved in AI-mediated
learning, and how does AI balance between providing challenging content and avoiding
cognitive overload?
•Scaffolding and Feedback Mechanisms: How does AI provide just-in-time feedback and
scaffold language learning effectively in informal contexts? What types of AI feedback
(e.g., corrective, formative) are most impactful on learner progress?
6) Cross-Disciplinary Approaches and Innovations
•Intersection with Gaming and Gamification: How does the use of game-like elements
or AI-driven gaming environments impact motivation and learning outcomes in informal
English learning?
•AI and Language Socialization: How can AI-driven tools help learners navigate and
participate in social contexts of language, such as casual conversations, slang, or idiomatic
expressions?
•Natural Language Processing (NLP) and Speech Recognition: Exploring how
advancements in NLP and speech recognition support accurate language practice,
pronunciation correction, and real-time interaction.
7) Affective Factors and Learner Motivation in AI-mediated Learning
•Emotional Engagement and Motivation: How do AI tools foster or hinder motivation
and engagement in informal learning? Are there specific design elements that encourage
sustained use?
•Social Presence and Interaction: How do AI-mediated platforms mimic real social
interactions, and what effect does this have on learner comfort and engagement?
•Reducing Anxiety: In what ways can AI provide a low-stakes, non-judgmental
environment that reduces anxiety for language learners, particularly beginners?
8) Cross-cultural Perspectives and Language Variation
•Multicultural and Multilingual Learners: How do AI-driven tools adapt to learners from
diverse cultural and linguistic backgrounds, especially regarding language norms, dialects,
and regional variations in English?
•Global Englishes and Language Models: How do AI tools handle different varieties of
English (e.g., British, American, Indian, Nigerian English) in informal settings? How does this impact learners’ understanding of English as a global language?
•Contextual Relevance and Cultural Sensitivity: How are AI tools culturally
contextualized for diverse learners, and how do they address cultural nuances and
contextual relevance in language learning materials?
9) Learner Identity and Personalization in AI-IDLE
•Identity Formation in Language Learning: How does the AI-mediated informal
environment support or challenge learners’ identities as English speakers? Does informal
AI learning influence learners’ self-perception in language use?
•Adaptive Learning Paths: How does AI effectively personalize learning paths based on
learner identity, goals, or specific needs (e.g., professional English, academic English)?
•Self-paced Learning and Autonomy: How does AI encourage learners to take ownership
of their learning and manage self-paced progress, especially when learning informally?
10) Sustainability and Long-term Impact of AI in Informal Learning
•Sustainability of Engagement: How do learners sustain engagement with AI tools over
time in informal contexts, especially in comparison with traditional learning environments?
•Learning Transfer and Retention: To what extent do language skills acquired through
AI-mediated informal learning transfer to real-life contexts, and how sustainable is this
knowledge?
•Adaptability to Changing AI Technologies: How do learners and educators adapt to rapid
developments in AI technology, and how does this adaptability affect the long-term impact
of AI-mediated learning?
11) AI-Driven Community and Collaborative Learning in Informal Contexts
•Peer Interaction and Social Learning: How does AI facilitate peer-to-peer interaction
and collaborative learning in informal settings?
•Community-Building and Social Platforms: What role do social media and community based AI-driven platforms (e.g., discussion forums, language exchange networks) play in
supporting English language learning?
•Collaborative AI Experiences: How can AI foster collaborative language tasks, such as
group discussions, interactive storytelling, or language games in informal learning
settings?
Types of Submissions
We encourage a range of submission types, including:
•Original Research Articles: Empirical studies that investigate aspects of AI-IDLE.
•Case Studies: In-depth explorations of specific AI-mediated informal learning
implementations.
•Review Articles: Comprehensive reviews of literature on AI-driven informal language
learning.
Target Audience
This special issue aims to reach researchers, educators, policymakers, and practitioners across fields such as applied linguistics, TESOL, educational technology, and AI in education. It will provide a platform for interdisciplinary dialogue on the opportunities and challenges of AI in informal English language learning, aiming to advance understanding and inform future research, policy, and practice.
Proposed Timeline
Submission Deadline: [June, 2025] Publication Date: [August, 2025]
All submissions must be sent via direct email to the corresponding guest editor, Dr. Ehsan Namaziandost at e.namazi75@yahoo.com; e.namazi75@gmail.com.