Developing metacognitive knowledge for effective AI-supported study behaviors in undergraduate biology: recommendations for instructors.

Vidya Adlakha, Madeline Arledge, Patrick McCay, Kailey Reeves, Fawaz Babatunde, Sharday N Ewell

Abstract read

In one paragraph

Article in Journal of microbiology & biology education, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Vidya Adlakha *Department of Biology, University of Mississippi, University, Mississippi, USA.

Madeline Arledge *Department of Biology, University of Mississippi, University, Mississippi, USA.

Patrick McCay *Department of Biology, University of Mississippi, University, Mississippi, USA.

Kailey Reeves *Department of Biology, University of Mississippi, University, Mississippi, USA.

Fawaz BabatundeDepartment of Biology, University of Mississippi, University, Mississippi, USA.

Sharday N EwellDepartment of Biology, University of Mississippi, University, Mississippi, USA.ORCID 0009-0004-7554-419X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The use of effective study behaviors is essential for student academic performance and persistence. Currently, it is reported that college students use generative artificial intelligence (AI) to help them prepare for courses, suggesting they hold metacognitive knowledge of AI as a study tool. However, there is no set of recommendations to develop metacognitive knowledge of effective AI-supported study behaviors for students enrolled in introductory biology courses. In this essay, we draw on existing literature on study strategies, metacognition, and emerging studies on AI use in higher education to consider how students use generative AI to support their learning and how incomplete metacognitive knowledge of AI use can result in continued use of ineffective study strategies that negatively influence academic performance. We then offer recommendations to support the development of this knowledge in undergraduate courses. As generative AI continues to become a mainstay within higher education, we hope this essay equips instructors with the knowledge to promote student success in biology coursework.

Indexed as

AI-supported learninggenerative AImetacognitionstudy strategiesundergraduate biology

Identifiers

PMID42059629

DOI10.1128/jmbe.00338-25

PMCPMC13523765

What OpenQuestion holds

Textmetadata

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.