Responsible Research and Innovation as a Novel Approach to Guide Educational Impact of Mind, Brain, and Education Research
Bibliographic record
Abstract
We propose a Responsible Research and Innovation (RRI) framework to improve the alignment between mind, brain, and education (MBE) research, the educational practice, and other societal stakeholders. RRI is an approach that has successfully been used in different research fields, but not yet in MBE research. After substantiating the need for, and possibilities of using this framework within MBE research, we report a case study to demonstrate the feasibility and benefits of RRI within an MBE context. This case study entails developing an educational intervention to improve learners' sense of agency regarding their own learning processes using neurofeedback. Using RRI, we found that societal stakeholders (teenagers, parents, and teachers) anticipate different potential impacts of this neurotechnology-based intervention than researchers did, enabling us to adapt the intervention according to these perspectives. This example demonstrates that RRI enables researchers to be reflexive and responsive to the stakeholders needs and values, to ultimately improve the educational and societal value of MBE research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.248 | 0.165 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.004 |
| Science and technology studies | 0.011 | 0.096 |
| Scholarly communication | 0.033 | 0.031 |
| Open science | 0.008 | 0.031 |
| Research integrity | 0.014 | 0.015 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".