Special Sessions

​​​​​CALL FOR SPECIAL SESSIONS

We invite proposals for Special Sessions that align with the conference theme “Architectures of the Future: Intelligent Technologies and Global Innovation". Each Special Session proposal must aim to attract at least 10 paper submissions, with a minimum of 5 accepted papers to be included in the final program. Accepted papers from these sessions will undergo the same rigorous review process as regular submissions.

Special Session 1: AI Meets Language: From Linguistic Intelligence to Machine Intelligence

This special session explores the dynamic intersection of artificial intelligence and linguistics, highlighting how advances in language technologies are reshaping our understanding of human and machine intelligence. It brings together diverse perspectives from theoretical linguistics, computational linguistics, phonetics, language engineering, natural language processing, and generative AI. The session welcomes research on large language models, multilingual and multimodal AI, Arabic and under-resourced languages, linguistic evaluation of AI systems, human–AI communication, and AI-assisted language learning. By fostering interdisciplinary dialogue, the session aims to examine how linguistic knowledge can advance more accurate, inclusive, culturally responsive, and human-centered AI technologies across languages and communities. The session may include topics according ,but not limited to the following axes:

1.  AI and Linguistic Theory.

• AI and theories of language and linguistic competence
• Computational perspectives on linguistic knowledge
• What LLMs reveal about the nature of language

2.  ⁠Phonetics, Phonology, and AI

• AI-based speech and acoustic analysis
• Automatic speech recognition and synthesis
• Prosody, intonation, and emotion recognition
• AI and speech disorders

3. ⁠Morphology and Computational Language Processing

• Morphological analysis and generation
• Inflectional and derivational morphology
• Morphologically rich languages and AI
• Arabic morphological processing

4. ⁠Syntax and AI

• Syntactic parsing and generation.
• Neural models and syntactic representations
• Syntactic ambiguity and AI
• AI and syntactic theory

5. Semantics, Meaning, and Large Language Models

• Lexical and compositional semantics
• Semantic representation in LLMs
• Polysemy, ambiguity, metaphor, and figurative language
• AI and the problem of linguistic meaning

6. ⁠Pragmatics, Discourse, and AI

• Context and pragmatic interpretation Implicature, presupposition, deixis, and inference
• Conversational AI and discourse coherence AI-generated discourse

7. Sociolinguistics, Language Variation, and AI

• Dialects and regional varieties
• Language, identity, and AI
• Linguistic diversity and representation
• Bias and discrimination in language models

8. Corpus Linguistics and AI

• AI-assisted corpus construction and annotation
• Automatic linguistic annotation
• Learner and multilingual corpora
• Corpus-based evaluation of LLMs

9. ⁠Arabic Linguistics and Generative AI

• Arabic LLMs and language-specific challenges
• Classical Arabic, Modern Standard Arabic, and dialects
• Egyptian Arabic and other Arabic varieties -Arabic morphology, syntax, semantics, and pragmatics in AI
• Arabic linguistic resources and benchmarks

10. Multilingualism and Cross-Linguistic AI

• Multilingual language models
• Cross-linguistic transfer
• Low-resource and under-resourced languages
• Translation and intercultural communication

11.  ⁠AI and Language Acquisition

• AI and first/second language acquisition
• AI-assisted language learning
• Learner language and automated feedback
• AI and foreign-language teaching

12.  ⁠AI, Cognition, and Psycholinguistics

• Language processing in humans and machines
• Cognitive models of language and AI
• AI and theories of linguistic cognition
• Comparing human and machine language processing

13. ⁠Generative AI and Linguistic Creativity

• AI-generated language and creativity
• Style, register, genre, and voice
• Metaphor, narrative, and literary language
• Human–AI co-creation

14. ​​Evaluating Linguistic Intelligence in AI

• Linguistic benchmarks for LLMs
• Testing grammatical competence
• Semantic and pragmatic competence
• Hallucination and linguistic reliability

15.  ⁠Human versus AI linguistic performance

• Language Engineering and the Future of AI
• Computational lexicons and ontologies
• Knowledge representation
• Language resources and infrastructure
• AI-driven language technologies
• The future of linguistics in AI development

Special session 2 : AI and Language Disabilities: From Linguistic Assessment to Intelligent Assistive Technologies

This special session explores the emerging role of artificial intelligence in understanding, assessing, diagnosing, and supporting individuals with language and communication disabilities. It brings together perspectives from linguistics, clinical linguistics, speech-language pathology, cognitive science, natural language processing, and generative AI. The session focuses on how AI can identify linguistic patterns associated with developmental and acquired language difficulties and provide personalized, accessible interventions. Particular attention is given to speech and language disorders, developmental language disorder, dyslexia, aphasia, communication difficulties associated with autism, and disorders of speech production. The session also addresses ethical issues, linguistic diversity, privacy, bias, and the development of inclusive AI technologies. The session may include topics according ,but not limited to the following axes:

 1.⁠ ⁠AI and Clinical Linguistics

• AI-assisted linguistic assessment
• Computational clinical linguistics
• Linguistic biomarkers of language disorders
• Automated analysis of speech and language

 2.⁠ ⁠AI and Speech Disorders

• Automatic detection of speech disorders
• Acoustic and phonetic analysis
• Speech recognition for atypical speech
• AI-assisted speech therapy

 3.⁠ ⁠AI and Developmental Language Disorders

• Early identification of language difficulties
• Developmental Language Disorder (DLD)
• AI-based language assessment in children
• Longitudinal monitoring of language development

 4.⁠ ⁠AI, Dyslexia and Reading Disabilities

• Automatic identification of dyslexia
• AI-assisted reading and writing
• Natural language processing for literacy difficulties
• Personalized intervention systems

 5.⁠ ⁠AI, Autism and Communication

• Analysis of linguistic and conversational patterns
• Social communication and pragmatic difficulties
• AI-supported communication technologies
• Natural language processing for autistic communication

 6.⁠ ⁠AI and Aphasia

• Automatic assessment of acquired language disorders
• Speech and discourse analysis
• AI-assisted rehabilitation
• Personalized language-recovery systems

 7.⁠ ⁠Generative AI and Language Disabilities

• ChatGPT and other LLMs as assistive technologies
• Adaptive language generation
• Simplification and personalization of communication
• Generative AI for therapy and educational support

 8.⁠ ⁠AI, Linguistic Assessment and Diagnosis

• Automated language testing
• NLP-based diagnostic tools
• Reliability and validity of AI assessment
• Human clinician–AI collaboration

 9.⁠ ⁠Multilingual and Arabic Language Disabilities

• AI for Arabic-speaking individuals with language disabilities
• Arabic dialects and clinical assessment
• Arabic speech and language datasets
• Challenges of morphologically rich languages

-       AI for under-resourced clinical languages

10.⁠ ⁠Inclusive and Accessible AI

• AI for augmentative and alternative communication (AAC)
• Assistive communication technologies
• Inclusive human–AI interaction
• Designing AI for diverse linguistic abilities

11.⁠ ⁠Ethical Issues and Responsible AI

• Privacy of clinical speech and language data
• Bias in AI-based assessment
• Representation of linguistic and disability diversity
• Explainability and clinical accountability