Participation

Researchers and practitioners may submit any of the following types of contributions:

• Empirical contributions: Submissions showcasing empirical studies or case studies exploring the use of AI and LA for teacher learning. This includes work examining interaction patterns between teachers and AI or the use of AI/LA to provide, capture, and assess teacher learning.

• Conceptual contributions: Discussion or position papers that challenge assumptions, raise critical questions, or propose new frameworks for assessing and supporting teacher learning through human-AI collaboration and LA. These contributions are intended to foster reflection and could guide the discussion during the workshop.

• Tools and demonstrations: Papers or demos showcasing interactive tools, dashboards, prototypes, or analytics platforms designed to assess, visualise, or support teacher learning.

• Practitioner reports: Experiences deploying AI/LA tools in real teacher trainings or learning situations.

Submissions will be double-blind peer-reviewed by the organizing committee. Accepted papers will be presented and discussed during the workshop.  

Accepted papers will NOT be published in the LAK companion proceedings; instead, proceedings shall be submitted to CEUR-WS.org for online publication. During the workshop, we can also discuss the possibility of submitting a more elaborated form of such papers as a Special Issue. 

All participants are kindly asked to complete a pre-workshop questionnaire using the paper submission button below. 

TAILA 2027 welcomes empirical, conceptual, technical, and practice-oriented contributions addressing the intersection of AI, LA, and teacher learning.

Authors are invited to submit contributions in one of the following formats:

• Full papers (10–14 pages): Substantial empirical, theoretical, or methodological contributions presenting mature research findings, conceptual frameworks, or evaluated AI/LA tools supporting teacher learning.

• Short papers (5–9 pages): Focused contributions, including preliminary empirical findings, position papers, work-in-progress studies, emerging conceptual frameworks, prototypes and demonstrations, and practitioner reports.

Both formats welcome contributions at different stages of research development, provided they clearly articulate their relevance to teacher–AI complementarity and/or the role of LA in supporting and assessing teacher learning.

Submissions must follow the official one-column CEURART template, which can be downloaded from http://ceur-ws.org/Vol-XXX/CEURART.zip. The file contains the LaTeX CEURART style and also the ODT (LibreOffice) template file. More information on formatting here and here. Page limits include references. 

All submissions should be anonymized, as they will undergo peer review based on relevance, originality, methodological or conceptual soundness, and potential to stimulate discussion during the workshop.

Accepted papers shall be submitted for publication in CEUR Workshop Proceedings, subject to the publisher's requirements and approval.

All deadlines are 23:59 AoE

• Submission opens: October 31st 2026

• Abstract submissions: December 4th 2026

• Notification of acceptance: December 18th 2026

• Registration: January 15th (Early bird)

• Camera-ready workshop papers: TBD

• Submitting workshop participation form: February 20th 2027