Date
Publisher
arXiv
Providing effective, personalized support is critical for helping students
overcome conceptual difficulties in physics. However, established scaffolding
methods, such as structured tiered support, are often too resource-intensive
for widespread implementation. Therefore, this study, investigates whether an
easily adaptable, custom-configured AI chatbot can offer comparable affective
benefits and cognitive relief. We conducted a quasi-experimental field study
with 273 ninth-grade students in Germany. Classes were randomly assigned to
solve a buoyancy problem using one of three conditions: an AI chatbot, a tiered
support system, or traditional textbook-style explanations. We measured
intrinsic and extraneous cognitive load and affective outcomes (enjoyment,
hope, hopelessness, self-efficacy, situational interest) via research-validated
questionnaires. Results revealed that both interactive support systems -- the
custom-configured AI chatbot and tiered hints -- were significantly more
effective than the textual support in reducing students' intrinsic and
extraneous cognitive load. Furthermore, the AI chatbot yielded the most
comprehensive affective benefits, demonstrating significant improvements across
all measured affective dimensions, when compared to the textual support. While
the chatbot consistently trended more positively than the tiered hints on
affective measures, these differences were not statistically significant. These
findings suggest that while structured guidance is key to managing cognitive
load, the interactive and social nature of AI chatbots holds unique potential
for simultaneously fostering positive affective experiences, marking a
promising direction for developing effective and holistic learning support
tools in physics education.
Study at a glance
- Question: How do AI chatbots, tiered hints, and textbook explanations differ in affecting students' cognitive load and emotions in physics?
- Setting and data: Classrooms in Germany, 273 ninth-grade students solving a buoyancy physics problem.
- Method: Quasi-experimental field study with pre- and post-test surveys (cluster randomization at class level).
Why it matters
Personalized support helps students overcome conceptual difficulties in physics, but creating structured tiered support is resource-intensive for teachers. Adaptable AI chatbots could offer scalable support, yet their cognitive and affective effects compared to established methods remain unexamined.
Key findings
- Both the AI chatbot and tiered support significantly reduced students' intrinsic and extraneous cognitive load compared to traditional textbook-style explanations.
- The AI chatbot produced significant improvements across all measured affective outcomes—enjoyment, hope, reduced hopelessness, self-efficacy, and situational interest—compared to textbook explanations.
- Although the AI chatbot consistently trended more positively on affective measures than tiered hints, the differences between the two interactive formats were not statistically significant.
Summary drafted with AI from the full paper and may contain errors. See the full paper for details.
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Study design
