Team Education

<p>The video reviews Team Education's exploratory AI project, highlighting both its rapid prototyping with modern tools and the importance of agile reflection through weekly blogs, despite time and resource challenges.</p>
<p>The team navigated limited access to real data, maintained a strong focus on ethics and legal boundaries—especially with sensitive technologies like facial recognition—and showcased creative, collaborative problem-solving. Viewers are encouraged to check out more of Team Education's related videos.</p>
<h4>Key takeaways:</h4>
<ul>
<li>Rapid AI prototyping benefits from prompt engineering skills.</li>
<li>Regular reflection helps learning but demands resources.</li>
<li>Real data enhances depth but may be restricted.</li>
<li>Ethical and legal limits guide AI development.</li>
<li>Collaboration and creativity drive innovative results.</li>
</ul>

<p>Team Education presents an overview of exploratory AI projects developed for Chalmers' educational context, focusing on tools like the Smart Canvas Assistant, assessment support, project guidance, and quality assurance.</p>
<p>The team aims to inspire deep learning by supporting teachers as facilitators and promoting meaningful information processing through AI-driven solutions. Close stakeholder collaboration has ensured the tools are relevant and practical. Viewers are encouraged to explore detailed prototype videos and a reflective discussion on the summer project.</p>
<h4>Key takeaways:</h4>
<ul>
<li>AI tools designed to inspire deep, meaningful learning</li>
<li>Focus on supporting teachers as learning facilitators</li>
<li>Prototypes include assessment and project guidance solutions</li>
<li>Strong stakeholder involvement ensures practical impact</li>
</ul>

<p>The video introduces EduCraft AI, a tool designed to help teachers efficiently create structured and pedagogically sound Canvas course rooms. The system leverages AI to suggest templates and layouts based on evidence-based frameworks, while allowing teachers to customize content and retain autonomy. The tool uses an ontology and probability-based approach to ensure consistency and reliability, and incorporates the ADDIE instructional design model. It also factors in principles from didactics, neuroscience (like cognitive load theory), and instructional design for quality assurance.</p>
<p>EduCraft AI aims to streamline course building, promote uniform structure, enhance learning outcomes, and facilitate course reusability for scenarios like upskilling. The tool ultimately helps both novice and experienced teachers create clearer, more effective online learning environments.</p>
<h4>Key takeaways:</h4>
<ul>
<li>EduCraft AI assists teachers in building Canvas courses with pedagogical templates.</li>
<li>Combines quality frameworks like ADDIE with teacher customization.</li>
<li>Uses AI for consistent, evidence-based course design.</li>
<li>Supports content reuse and benefits both students and teachers.</li>
</ul>

<p>The video introduces "Flow," an AI-powered discussion facilitator developed by Team Education. Flow transcribes conversations in real-time, uses frameworks like Bloom’s taxonomy and Socratic questioning to generate follow-up questions, and works both automatically and manually during discussions. The tool helps deepen understanding, supports a variety of meeting types, allows customization of context, language, and question frequency, and enables session summaries.</p>
<p>Its modular design enables integration with different transcription engines and frameworks, enhancing flexibility and precision. Future developments include voice recognition, engagement analysis, and expanded context input to improve both the quality of questions and user experience.</p>
<h4>Key takeaways:</h4>
<ul>
<li>Flow generates context-aware questions using educational frameworks.</li>
<li>Works in multiple languages for seminars and meetings.</li>
<li>Allows manual and automatic question prompts.</li>
<li>Enables transcription, summaries, and is highly customizable.</li>
<li>Designed for flexibility and future expansion.</li>
</ul>

<p>The video introduces Smart Project Guidance and its core tool, ReflectBob—an AI-supported feedback system designed to help Teaching Assistants (TAs) in project-based courses at Chalmers. ReflectBob aims to address TA time constraints and varying competencies by automating structured feedback and formative assessment, grounded in educational frameworks like Timperley and Hattie’s feedback model, Bloom’s taxonomy, and scaffolding techniques. The system combines AI-driven reflection prompts and feedback tools for both students and TAs, enhancing reflective practice and feedback quality.</p>
<p>The team emphasizes the importance of clear prompt engineering and the need for human oversight to ensure useful outputs. ReflectBob is presented as a modular, adaptable tool, but the team notes challenges in ensuring output relevance and encourages iterative testing for improvement.</p>
<h4>Key takeaways:</h4>
<ul>
<li>AI can help TAs give structured feedback and support student reflection.</li>
<li>Clear, specific prompts improve AI output relevance.</li>
<li>Pedagogical frameworks guide the AI’s analysis.</li>
<li>Human oversight remains essential for quality control.</li>
<li>The tool is modular and suited for iterative testing and refinement.</li>
</ul>

<p>The video introduces MathGrader, an AI-based tool designed to assist teachers in evaluating handwritten math solutions by converting scans into digital text using OCR and analyzing them with a language model.</p>
<p>MathGrader provides transcriptions, assesses correctness, identifies strengths and weaknesses, suggests scores, and offers feedback. While effective with clear handwriting, its accuracy declines with poorer handwriting, underscoring the need for a custom-trained OCR model on local data.&nbsp;</p>
<p>The tool aims to save teachers time, enhance feedback consistency, and support both formative and summative assessments. Potential future improvements include bulk assessment, custom rubrics, and further integration with teaching workflows.</p>
<h4>Key takeaways:</h4>
<ul>
<li>MathGrader digitizes and assesses handwritten math solutions.</li>
<li>OCR and language models automate feedback and scoring.</li>
<li>Accuracy improves with clear handwriting; local data could enhance results.</li>
<li>The system can support both exams and classroom diagnostics.</li>
<li>Further development could expand capabilities and usability.</li>
</ul>

<p>The video presents Team Education’s “Assessment Support” project, exploring how AI can assist teachers in grading student texts through a prototype tool. The team researched AI strengths, market options, and educational needs, then designed a system to analyze, highlight, and grade student work, integrating both AI and teacher input. Teachers review AI-generated feedback and can adjust grades or comments, ensuring human oversight. Students receive clear, actionable feedback aimed at both immediate improvement and long-term learning.</p>
<p>The prototype emphasizes data-sensitive training for accurate, course-specific assessment and proposes future Canvas integration. The project highlights both the potential and challenges of using AI in summative assessment.</p>
<h4>Key takeaways:</h4>
<ul>
<li>AI can support, but not replace, teacher assessment.</li>
<li>Teachers maintain control over grading and feedback.</li>
<li>Student feedback is tailored and actionable.</li>
<li>Data privacy and course-specific training are essential.</li>
<li>Future integration with learning platforms is planned.</li>
</ul>

<p>The video presents an AI-driven Oral Exam System developed by Team Education to enhance assessment by capturing students’ real-time spoken responses and transcribing them instantly. The system uses AI to ask follow-up questions, adapts to student performance, and can evaluate knowledge on various topics, like machine learning. It is built on the earlier “Flow” tool and integrates pedagogical frameworks such as Bloom’s taxonomy and Socratic questioning to deepen learning.</p>
<p>The system can facilitate many simultaneous oral exams, offering potential for more secure and authentic assessment compared to written or take-home exams. While it raises legal and quality control considerations, it serves as a valuable diagnostic and preparatory tool, supporting responsive, continuous teaching and helping teachers identify knowledge gaps throughout a course.</p>
<h4>Key takeaways:</h4>
<ul>
<li>Real-time AI-driven oral assessment adapts to student responses.</li>
<li>Integrates pedagogical frameworks for deeper learning.</li>
<li>Potentially more secure than traditional exams.</li>
<li>Useful for both ongoing diagnostics and exam preparation.</li>
</ul>

<p>Team Education presents AI-driven solutions to enhance course quality assurance at Chalmers University. They identify key steps in the process—collecting and preprocessing feedback, evaluation meetings, and follow-up actions—and suggest integrating AI to automate tasks such as filtering comments, summarizing feedback, and analyzing sentiment.</p>
<p>Their demo showcases a tool that compares survey data over time and generates summary reports, streamlining administrative work. They also advocate for gathering more continuous feedback and integrating diverse data sources, moving beyond surveys to better assess real course quality. However, they acknowledge the complexity of educational quality and the need for further development.</p>
<h4>Key takeaways:</h4>
<ul>
<li>AI can automate time-consuming feedback analysis and reporting.</li>
<li>Continuous and multi-source feedback offers deeper insights.</li>
<li>Quality assessment must consider complex, dynamic course factors.</li>
<li>Inter-department collaboration could improve quality assurance.</li>
</ul>

<p>The video introduces the "Golden Nugget Assignment Analyzer," a tool developed by Team Education to help educators efficiently identify, reuse, and share high-quality assignments ("golden nuggets") within and across Canvas courses. Using AI, the tool analyzes course data—like student completion rates and engagement—alongside mock data, to score and suggest valuable teaching materials, which can then be adapted to new courses or subjects, drawing on Bloom's taxonomy for relevance.</p>
<p>The team envisions expanding the platform into a broader resource library, to share and save effective templates and learning activities campus-wide, supporting both experienced and new educators in instructional design and reflective teaching documentation.</p>
<h4>Key takeaways:</h4>
<ul>
<li>The tool finds and scores high-value assignments in Canvas courses.</li>
<li>It enables adaptation and reuse of materials across subjects and levels.</li>
<li>AI-driven analysis is based on engagement, completion, and feedback.</li>
<li>Future plans include a community resource library for broader sharing.</li>
<li>The system supports reflective teaching and instructional documentation.</li>
</ul>

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Team Data Exploit

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Explorative AI projects 2025

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Explorative AI projects 2025

Team Education

Introduction to Team Education

Video description: Introduction till Team Education

Overview of the project

Video description: Overview of the project

Smart Canvas assistant

Video description: Smart Canvas assistant

FLOW discussion facilitator

Video description: FLOW discussion facilitator

Smart Project Guidance

Video description: Smart Project Guidance

Assessment tool mathematics

Video description: Assessment tool mathematics

Assessment tool text

Video description: Assessment tool text

Oral assessment tool

Video description: Oral assessment tool

Quality assurance

Video description: Quality assurance

Find the golden nuggets

Video description: Find the golden nuggets