While AI has gained widespread attention in education with the rise of generative AI, it has long been integral to many EdTech tools. The 5Es framework applies across all types of tools, including those with AI. However, AI-heavy tools, given their reliance on complex algorithms and data models, present unique challenges and considerations within each component of the 5Es. We therefore outline how these AI-intensive EdTech tools align with each of the 5Es dimensions, highlighting specific impact areas and considerations.
Efficacy:
The fundamental question in this impact strand is: “Does the EdTech solution work?” For AI tools, efficacy evaluation focuses on learning outcomes achieved through rigorous experimental testing. As with all EdTech tools, the goal is to measure actual learning gains or behavioural changes, but with AI-driven solutions, the evaluation must often include additional data to assess the adaptive mechanisms unique to AI.
Effectiveness:
The guiding question in Effectiveness is: “Could the solution work?” Effectiveness evaluations explore how AI tools perform in real-world educational settings, emphasizing teacher engagement, adaptability, and user experience rather than strict, standardized assessments. This dimension looks at the dynamic interactions between educators and AI, assessing how teachers’ inputs and adaptations shape outcomes when AI is integrated into classroom contexts.
Equity:
Equity is a critical concern for AI-based EdTech tools due to well-documented biases in the data on which AI models are trained. These biases can disadvantage marginalized groups that are already underserved in traditional educational settings. The 5Es framework examines the representation of these groups in AI-driven solutions, assessing whether diverse student populations benefit equitably from these tools and whether biases are minimized in both development and deployment.
Ethics:
This vertical is particularly essential for AI due to its dependency on user data. Ethical evaluation centers on how AI-driven EdTech handles personal data, focusing on the collection, management, storage, and sharing of user information. Given the significant privacy implications, AI tools are subjected to more comprehensive ethical scrutiny than non-AI tools, examining adherence to data protection standards and transparency in data use.
Environment:
The environmental impact of AI-powered EdTech tools is a growing concern, particularly in relation to energy consumption and resource use associated with data processing. This impact vertical assesses the sustainability of AI-driven solutions, scrutinizing the environmental costs of deploying data-heavy models and large-scale computations. Given AI’s substantial energy requirements, this dimension calls for careful consideration of sustainable practices and potential trade-offs in environmental impact.