Research analysis

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The critical examination and interpretation of research data, methodologies, and findings. 

Type = Research analysis

Category = Inquiry or investigation – Open

Description

Research analysis assessments have students engage critically with the research process. This may include critiques of published research, or analyses of research data, or even conducting small research projects. These assessments develop key skills such as formulating research questions, conducting literature reviews, designing methodologies, collecting and analysing data, interpreting findings, and presenting key findings within their field of study. These assessments expose students to the challenges of real research, including developing rigorous methodologies to ensure valid and reproducible results, adhering to ethical and legal regulations, managing time and funding constraints, and effectively disseminating findings. They also provide an opportunity for students to explore research firsthand and evaluate their interest in pursuing it further.

Research analysis assessments are present across all academic disciplines and are tailored to align with the methodologies and frameworks specific to each field. Typical formats include written reports, papers, posters and funding proposals, which are sometimes paired with a presentation assessments or interactive orals. The outputs of these assessments can enhance students' professional portfolios, demonstrating their ability to analyse data, utilise industry-standard tools, and translate research findings into practical insights.

Meaningful engagement with AI

AI can be integrated into several stages of a research analysis assessment, offering students an opportunity to learn to use AI tools to enhance their research capabilities rather than replace them. Below are examples of how AI can be utilised at various stages of a research analysis assessment.

  • AI can assist in summarising existing literature and identifying key research papers.
  • AI tools can offer feedback on students' methodology, assessing their feasibility and validity. Additionally, AI can prompt students to consider crucial factors such as variable control, ethical considerations, bias minimisation, and error reduction.
  • AI can assist students by generating code to automate data cleaning, conduct analyses, and create visualisations. However, students must apply their technical expertise to critically assess AI-generated results, ensuring the analysis is accurate and appropriately conducted.
  • AI can assist students in organising and articulating their findings clearly and concisely, ensuring key results are effectively communicated using technical language as appropriate for the intended audience.

Before starting the assessment, educators should discuss with students the relevant ethical and legal implications of sharing data with AI tools, emphasising the importance of data privacy, confidentiality, and compliance with existing regulations.

Educators should then provide students guidance on the effective use of AI tools in research analysis, emphasising that AI can introduce errors, produce invalid analyses, or lead to inaccurate conclusions, especially in emerging research fields where AI knowledge may be incomplete or outdated. They should make clear that are expected to apply their own reasoning, discipline-specific knowledge, and contextual understanding when using AI-generated outputs. Instructors should stress that students bear full responsibility for their final work and must ensure the accuracy and reliability of their results.

Group work opportunities

Research analysis frequently involves collaborative group work, uniting individuals from diverse disciplinary backgrounds with specialised expertise. To ensure the project's success, students must develop effective communication skills and learn to adapt to different personalities and working styles. Additionally, they need to strategically plan and allocate tasks to leverage the team’s collective strengths, optimising efficiency and outcomes. This team-based approach reflects real-world scientific and industry practices, adding authenticity to the assessment and preparing students for future collaboration.

Other resources

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