

Anthony Daoudi
Educational Technology Specialist
March 15, 2025
Last week, I attended Toddle Demo Day 4.0—and let's just say, I walked in skeptical but left thinking twice about AI-assisted grading. As educators, we've all experienced the late nights spent meticulously reviewing student work—but what if technology could not only lighten this load but actually enhance the quality and consistency of our feedback?
Before diving into AI solutions, let's acknowledge the reality facing educators today. A 2022 study by the National Education Association found that teachers spend an average of 7-12 hours weekly on assessment activities alone. For IB and AP teachers, this time commitment often increases substantially due to the depth of analysis required for higher-level assessments.
Traditional grading faces three persistent challenges:
The Toddle 4.0 Demo Day offered a fascinating look at how artificial intelligence is being implemented to address these challenges. Their AI Grading Assistant demonstrated capabilities that extend far beyond simple automated scoring:
My aha moment? Seeing how AI can handle complex, open-ended responses with a surprising level of accuracy. It's not about handing over the reins—it's about making grading faster, fairer, and freeing up time for what matters most: teaching.
The movement toward AI-assisted grading isn't merely a technological convenience—it's supported by emerging research. A 2023 study published in the Journal of Educational Technology & Society examined AI assessment tools across 17 different educational contexts and found:
Similar findings were reported by researchers at Carnegie Mellon's Human-Computer Interaction Institute, who noted that AI-assisted approaches allow teachers to focus more on higher-order feedback elements while the AI handles more routine aspects of assessment.
For IB educators, the implications are particularly significant. The IB's emphasis on criterion-referenced assessment aligns perfectly with AI systems trained on specific rubrics. Several presenters at the Toddle Demo Day discussed pilot implementations in IB contexts:
For AP courses, similar applications were highlighted, with particular success in content-heavy subjects where the AI can verify factual accuracy while teachers focus on evaluating higher-order thinking.
No discussion of AI in education would be complete without addressing ethical considerations. The Toddle Demo Day panels thoughtfully explored several key concerns:
The consensus among educators at the event was that a "human-in-the-loop" approach—where AI makes recommendations but teachers maintain final authority—represents the most responsible implementation model.
For educators interested in exploring this technology, the Toddle presenters suggested several entry points:
Many educators reported starting with formative assessments before implementing AI assistance for summative evaluation.
As we look ahead, it's clear that AI-assisted grading represents not just a technological shift but a pedagogical one. By reducing the mechanical aspects of assessment, these tools create space for the elements of teaching that most deeply impact student growth: mentorship, personalized guidance, and responsive instruction.
The most exciting possibility, highlighted by several Toddle Demo Day speakers, is how AI might help bridge the historical divide between summative and formative assessment. When grading becomes more efficient and consistent, assessment can more easily serve its dual purpose—not just measuring learning but actively promoting it.
At Bespoke Learning, we're actively exploring how these technologies can enhance our tutoring and educational support services. We'd love to hear your thoughts on AI-assisted grading and assessment. Would YOU trust AI to grade your students' work? What possibilities or concerns do you see in this evolving landscape?
Connect with us to continue the conversation about how we can collectively harness these innovations to benefit students in IB, AP, and other rigorous academic programs.
National Education Association. (2022). Assessment Time Study: Teacher Workload and Student Feedback Quality.
Stanford Graduate School of Education. (2022). Consistency in Educational Assessment: A Multi-Teacher Analysis.
Journal of Educational Technology & Society. (2023). AI Assessment Tools in Educational Contexts: A Comparative Analysis.
Carnegie Mellon Human-Computer Interaction Institute. (2023). Human-AI Collaboration in Educational Assessment.
Toddle. (2024). Demo Day 4.0: AI-Assisted Grading Tools Presentation Materials.