Evaluating AI-Driven EdTech Tools: Research Methods and Pedagogical Implications

EasyChair Preprint 13867

Abstract

Evaluating AI-driven EdTech tools involves employing rigorous research methods to assess their efficacy and impact on education. Researchers typically utilize a combination of quantitative analysis, such as measuring student performance metrics and learning outcomes, and qualitative approaches, such as observing classroom dynamics and gathering user feedback. This comprehensive evaluation helps identify strengths, weaknesses, and areas for improvement in these tools. Pedagogically, integrating AI-driven EdTech can enhance personalized learning experiences, adapt content delivery to student needs, and foster engagement through interactive learning environments. However, it also raises concerns about data privacy, equitable access to technology, and the need for educators to receive adequate training. Balancing these factors is crucial for maximizing the potential of AI-driven EdTech in enhancing educational outcomes while addressing its associated challenges.

Keyphrases: AI-driven EdTech, Research methodologies, evaluation methods

@booklet{EasyChair:13867,
  author    = {James Henry and Lukas Zavier},
  title     = {Evaluating AI-Driven EdTech Tools: Research Methods and Pedagogical Implications},
  howpublished = {EasyChair Preprint 13867},
  year      = {EasyChair, 2024}}