Shared Tasks as Tutorials: A Methodical Approach

Source publication / research team or educational organization described in paper

Italy, Germany

Researchers, educators, instructors, or facilitators as described in the source publication

• Research / curriculum design context

Course implementation or course design

Not specified in extracted text

Not specified in extracted text

Robotics / physical AI, Assessment / tutoring analytics

• The paper provides limited implementation detail in the extracted abstract; additional manual review may be needed for local replication.

Unspecified / broad education

Mixed or not explicitly specified; infer from target learner group and intervention design.

Varies by intervention; not specified unless the paper explicitly describes prerequisites.

• Document the AI education intervention, course, tool, or resource described in the source publication.
• Extract the learner context, AI role, pedagogy, outcomes, and constraints for AAB registry comparison.
• In this paper, we discuss the benefits and challenges of shared tasks as a teaching method.

• Support AAB comparison across AI literacy, AI education, teacher training, higher education, and workforce contexts.
• Capture evidence maturity, transferability, and limitations rather than treating the publication as product endorsement.

• Not an AAB endorsement of the tool, curriculum, provider, or result.
• Not a direct replication record unless the source paper reports implementation details sufficient for replication.

Robotics / physical AI, Assessment / tutoring analytics

Language context discussed in source publication

• Tutor
• Co-creator
• Automation tool

• Primary interaction pattern inferred from publication: Curriculum / course design, Physical AI / robotics learning.
• AI capability focus: Robotics / physical AI, Assessment / tutoring analytics.

• Apply standard AAB safeguards: privacy, transparency, human oversight, and documentation of limitations.

• Review the publication’s reported context, learner group, AI tool or curriculum, implementation process, and outcome evidence.
• Map the case to AAB registry fields for comparison across educational levels and AI capability types.
• Use the source publication and PDF for any manual verification before public registry release.

• Human educators/researchers remain responsible for instructional design, supervision, interpretation, and ethical safeguards.
• AI systems or AI concepts provide the learning object, support tool, evaluator, simulator, or automation context depending on the paper.

• Tutoring / feedback-supported learning
• Registry extraction emphasizes explicit learning goals, observed outcomes, constraints, and safety limitations.

• The paper provides limited implementation detail in the extracted abstract; additional manual review may be needed for local replication.

• Case classified under: Published empirical study.
• Pedagogical pattern: Tutoring / feedback-supported learning.
• Any additional adaptations should be verified against the full paper before public-facing publication.

• Engagement evidence should be interpreted according to the source paper’s reported method and sample.
• A shared task is a scientific event and a friendly competition to solve a research problem, the task.

• A shared task is a scientific event and a friendly competition to solve a research problem, the task.

In this paper, we discuss the benefits and challenges of shared tasks as a teaching method. A shared task is a scientific event and a friendly competition to solve a research problem, the task.

• Apply standard AAB safeguards: privacy, transparency, human oversight, and documentation of limitations.

• Activity documentation

• Can be used as an AAB evidence record for cross-case comparison, standards drafting, and evidence-maturity mapping.
• Supports identification of recurring patterns in AI literacy, AI education implementation, teacher preparation, assessment, and responsible AI learning.

• Implementation guidance
• Curriculum / course design
• Physical AI / robotics learning
• Robotics / physical AI
• Assessment / tutoring analytics

• Completed

Unspecified / broad education

Research / curriculum design context

Robotics / physical AI, Assessment / tutoring analytics

Tutoring / feedback-supported learning

Low to Medium

Medium

Shared Tasks as Tutorials: A Methodical Approach

• Theresa Elstner
• Frank Loebe
• Yamen Ajjour
• Christopher Akiki
• Alexander Bondarenko
• Maik Fröbe
• Lukas Gienapp
• Nikolay Kolyada
• Janis Mohr
• Stephan Sandfuchs
• Matti Wiegmann, Jörg Frochte
• Nicola Ferro
• Sven Hofmann
• Benno Stein
• Matthias Hagen
• Martin Potthast

Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 37 No. 13, EAAI-23

2023

10.1609/aaai.v37i13.26877

https://ojs.aaai.org/index.php/AAAI/article/view/26877

https://ojs.aaai.org/index.php/AAAI/article/view/26877/26649

068_Shared Tasks as Tutorials_ A Methodical Approach.pdf

9

In this paper, we discuss the benefits and challenges of shared tasks as a teaching method. A shared task is a scientific event and a friendly competition to solve a research problem, the task. In terms of linking research and teaching, shared-task- based tutorials fulfill several faculty desires: they leverage students’ interdisciplinary and heterogeneous skills, foster teamwork, and engage them in creative work that has the potential to produce original research contributions. Based on ten information retrieval (IR) courses at two universities since 2019 with shared tasks as tutorials, we derive a domain- neutral process model to capture the respective tutorial struc- ture. Meanwhile, our teaching method has been adopted by other universities in IR courses, but also in other areas of AI such as natural language processing and robotics.

• Research / curriculum design context

• The paper provides limited implementation detail in the extracted abstract; additional manual review may be needed for local replication.

Not specified in extracted text unless noted in duration field.

Requires educators/researchers/facilitators with sufficient AI literacy and pedagogy knowledge for the target learners.

Infrastructure depends on AI tool type, learner devices, data access, and institutional policy context.

High

• group_size
• duration

This entry was automatically extracted from the PDF text and manifest metadata. Fields should be manually verified before public registry publication, especially group size, location, duration, and outcome claims.