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001			3120-4872
003			DE-He213
005			20260304140622.0
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022			_a3120-4872
024	7		_a44568 _2local
210	1	0	_aCureus J AI-Aug Res
245	1	0	_aCureus Journal of AI-Augmented Research _h[electronic resource] / _cedited by Meghashri Sharath.
264		1	_aNew York : _bSpringer US : _bImprint: Springer Nature.
300			_bonline resource.
520			_aJournal Mission The Cureus Journal of AI-Augmented Research (CJAI) publishes high-quality research that demonstrates how artificial intelligence is advancing scientific inquiry across all disciplines. CJAI serves as a home for researchers using AI to push the boundaries of what's possible in their fields, while maintaining rigorous standards for transparency, reproducibility, and validation. What We Publish CJAI welcomes submissions that showcase AI's role in research through: AI Methods and Application Research demonstrating how AI techniques enable new analyses, improve research efficiency, or achieve outcomes not possible with traditional methods. This includes Novel AI applications in any research domain (healthcare, materials science, climate, social sciences, etc.) highlighting how new and innovative AI methodologies improve the performance within the chosen application domain. Validation studies comparing AI approaches to traditional methods Methodological advances adapting AI techniques for specific research challenges Tool and algorithm development for research applications Reproducibility studies validating or extending previous AI research Negative results documenting when and why AI approaches did not improve outcomes AI Impact and Implications Research evaluating AI's broader effects on research practices, scientific workflows, and societal outcomes, including: Workflow and productivity studies measuring AI's impact on research efficiency Implementation research examining barriers and facilitators to AI adoption Ethical analyses of AI use in research contexts Policy and governance studies informing responsible AI use Equity and access studies examining how AI affects research opportunities Comparative studies evaluating AI versus traditional research approaches Cross-Cutting Themes CJAI particularly values submissions that: Demonstrate clear AI contribution - Show what AI uniquely enables Emphasize reproducibility - Provide code, data, and detailed methods Include rigorous validation - Substantiate all performance claims Acknowledge limitations - Honestly discuss where AI falls short Consider broader implications - Discuss ethical, equity, or policy dimensions Share negative results - Help the community learn from what doesn't work .
650		0	_aArtificial intelligence.
650	1	4	_aArtificial Intelligence.
700	1		_aSharath, Meghashri. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt _924068
710	2		_aSpringerLink (Online service)
856	4	0	_uhttp://link.springer.com/journal/44568 _zOpen Access
999			_c579934 _d579934