AI for Assessment, Testing and Applied Measurement

Artificial Intelligence (AI) is an emerging technology that has tremendous potential to reshape the field of educational assessment, testing and applied measurement. AI systems leverage machine learning (ML) approaches to mimic various human cognitive behaviors, including extracting useful information from large data sets and delivering individualized content. These AI capabilities make it an attractive tool for tackling various tasks in educational assessment, testing, and applied measurement, particularly those that rely on large data sets to evaluate learners’ progress and proficiency. Further, recent advances in AI technology allow analyzing information from multiple modalities, including text, visual representation, or interactive modules among others to meet the needs of individual diverse learners. However, despite advanced capabilities, AI technology has considerable limitations, including potential biases that influence model outputs, data security and privacy issues that influence usefulness and applicability AI in educational assessment, testing and measurement, and other limitations.

This Research Topic seeks contributions that use AI to tackle challenges in educational assessment, testing and measurement broadly defined. The goal of this Research Topic is to shed light on the latest discoveries, new insights, novel developments, and future challenges in this advancing field.

Topics of interest include but are not limited to:

- automated scoring

- item evaluation

- validity studies

- formative and summative feedback

- automatic item generation

- precision and accuracy of AI approaches

- bias and fairness in using AI and their mitigation measures

- interpretability

- implications for evaluating educational outcomes

- ethics of AI

- issues of fairness, accountability, and transparency

- using AI to evaluate complex reasoning and skills across disciplines

This Research Topic welcomes empirical, conceptual, and review studies. Articles published in this Research Topic must make conceptual and/or empirical contributions to our knowledge about using AI in educational assessment, testing and measurement.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Conceptual Analysis
• Curriculum, Instruction, and Pedagogy
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• Hypothesis and Theory
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Conceptual Analysis
• Curriculum, Instruction, and Pedagogy
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Policy Brief
• Registered Report
• Review
• Study Protocol
• Systematic Review
• Technology and Code

Keywords: artificial Intelligence (AI), machine learning (ML), assessment, testing, applied measurement

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.