AI evidence extraction

Teacher support and student motivation to learn with Artificial Intelligence (AI) based chatbot

Thomas K. F. Chiu, Benjamin Luke Moorhouse, Ching Sing Chai, Murod Ismailov · Interactive Learning Environments · 2023

AI-generated evidence extraction, verified across multiple analytical personas. Not a substitute for the peer-reviewed original.

6/10

Relevance

1/4

Quality (LMQS)

Evidence

617

Citations

185.91

FWCI

Top 10%

Impact

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.1080/10494820.2023.2172044

Methodology & findings

Study design

Experimental study with 123 Grade 10 students using chatbots as AI-based technologies.

Sample

N = 123

Main result

The study found that "intrinsic motivation and competence to learn with the chatbot depended on both teacher support and student expertise (i.e. self-regulated learning and digital literacy), and the teacher support better satisfied the need for relatedness, and it less satisfied the need for autonomy." These findings demonstrate the critical role of teacher support in mediating the relationship between student expertise and motivation outcomes when learning with AI technologies.

Reports effect sizes.

Research paradigm

Mixed-methods (quantitative experimental design with psychological framework)

Author conclusions

The authors conclude that "the findings refined our understanding about the application of self-determination theory and expand the pedagogical and design considerations of AI application and instructional practices," indicating that teacher support plays a crucial mediating role in optimizing student motivation and competence development with AI-based learning technologies.

Risk of bias

Selection bias: Single Grade 10 cohort may not represent diverse student populations; Potential confounders: Student prior achievement, socioeconomic factors not controlled; Teacher effects: Different teachers may have varying support capabilities; Selection bias: recruitment and assignment procedures not detailed in abstract; Attrition: no information provided on participant dropout rates; Confounders: prior academic achievement, socioeconomic status not mentioned as controlled variables; Teacher effects: variation in teacher support quality and delivery not addressed; Selection bias: Single grade level (Grade 10) limits generalizability; Potential teacher effect bias: Teacher support variable may be confounded with teacher characteristics or classroom climate; Attrition risk: No mention of dropout or attrition rates; Measurement bias: Self-report measures of motivation and competence vulnerable to social desirability bias; Confounding variables: Digital literacy and self-regulated learning measured as student expertise but may correlate with other unmeasured factors

Limitations

The abstract and paper scope indicate this study was limited to "Grade 10 students" in a specific classroom context, which restricts generalizability
The authors note they examined "how teacher support moderates the effects of student expertise on needs satisfactions and intrinsic motivation to learn with AI technologies," suggesting the findings may be context-dependent and not universally applicable across all educational settings or grade levels.

Open questions raised

Weak connection between AI research in education and pedagogical perspectives in K-12 education
Need for understanding teacher's role in student motivation when learning with AI technologies
Limited research on how different levels of teacher support affect student motivation with AI
The authors identify that research on AI in education has shown "a weak connection to pedagogical perspectives or instructional approaches, particularly in K-12 education" and emphasize the need to understand "the teacher's role of student motivation in mediating and supporting learning with AI technologies in the classroom."
The authors identify that "research on AI in education reflects a weak connection to pedagogical perspectives or instructional approaches, particularly in K-12 education" and that "understanding the teacher's role of student motivation in mediating and supporting learning with AI technologies in the classroom is needed." They suggest future work should address how different types of teacher support and student characteristics interact in varied educational contexts.

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