Artificial Intelligence Across Disciplines: New Research Opportunities for Scholars in 2026

Artificial Intelligence Across Disciplines: New Research Opportunities for Scholars in 2026

Can artificial intelligence improve access to healthcare without deepening existing inequalities? How should educational institutions use generative AI while protecting academic integrity? Can AI-supported environmental modelling help vulnerable communities prepare for climate risks? What happens to accountability when organisations rely on algorithmic recommendations? How can historians, linguists, philosophers and cultural researchers contribute to the development of responsible AI?

These are not questions that one discipline can answer independently.

Research Horizons encourages multidisciplinary and interdisciplinary scholarship capable of examining artificial intelligence from technical, social, ethical, scientific and human perspectives. The journal invites scholars to treat AI not merely as a technological product, but as a research domain connecting knowledge across disciplines.

Why Artificial Intelligence Research Is Becoming More Interdisciplinary

Artificial intelligence systems operate within complex social and institutional environments. Their performance depends not only on algorithms, computing resources and training data, but also on how problems are defined, how data are collected, who is represented, how outputs are interpreted and who is affected by their use.

A technically accurate system may still produce harmful outcomes if it is deployed without understanding human behaviour, cultural context, organisational constraints or unequal access to technology. Similarly, an ethically well-intentioned AI project may fail if it lacks appropriate technical evaluation or realistic implementation planning.

This makes interdisciplinary collaboration increasingly important.

Computer scientists may contribute model design and technical evaluation. Social scientists can investigate adoption, power, inequality and institutional behaviour. Legal scholars can examine accountability, privacy and regulatory responsibilities. Philosophers can analyse autonomy, fairness and human dignity. Healthcare researchers can assess clinical relevance and patient safety. Education specialists can study learning outcomes and pedagogical implications.

The growing institutional adoption of AI also demonstrates why research must extend beyond laboratory performance. OECD data indicate that 20.2% of firms across countries with available data reported using AI in 2025, compared with 14.2% in 2024 and 8.7% in 2023. This expanding adoption creates research opportunities involving organisations, labour, productivity, governance, skills and public trust. OECD

Artificial Intelligence as a Tool and a Subject of Research

Researchers can approach artificial intelligence in two principal ways.

First, AI can be used as a research tool. Scholars may apply machine learning, natural language processing, computer vision, predictive modelling or generative systems to analyse information, identify patterns and support scientific discovery.

Second, AI can be studied as a social, technical or institutional phenomenon. Researchers may investigate its effects on employment, education, healthcare, creativity, law, public policy, communication and social relationships.

Many of the strongest interdisciplinary studies combine both perspectives. They use artificial intelligence to support an investigation while also critically evaluating the assumptions, limitations and consequences associated with that use.

The OECD has highlighted AI’s potential to process large-scale scientific data, support reproducibility, lower experimental costs and accelerate discovery. However, realising these benefits requires research on data quality, validation, infrastructure, skills and responsible governance. OECD report on Artificial Intelligence in Science

AI Research Opportunities in Education

Education is one of the most active areas for interdisciplinary AI research. Generative AI, automated assessment, intelligent tutoring, learning analytics and adaptive platforms are changing how students learn and how teachers design instruction.

UNESCO recognises that AI may help address educational challenges and support progress towards inclusive and equitable education. It also warns that technological development has moved faster than many policy and regulatory discussions. UNESCO Artificial Intelligence in Education

Important research questions for 2026 include:

• How does generative AI affect critical thinking and independent learning?
• Can AI-supported tutoring improve outcomes for underserved students?
• How should universities redesign assessment in an AI-enabled environment?
• What forms of AI literacy do teachers and students require?
• How can institutions protect learners’ privacy?
• Do automated assessment systems produce fair results across languages and social groups?
• How should educators disclose and evaluate AI-assisted academic work?
• Can AI improve accessibility for learners with disabilities?
• What is the relationship between AI use, motivation and student engagement?
• How can local languages and cultural contexts be represented in educational AI?

Researchers may combine education, psychology, computer science, linguistics, sociology, disability studies and public policy to address these questions.

The strongest studies will look beyond whether a tool works. They will investigate for whom it works, under what conditions, with what risks and according to which educational values.

AI Research Opportunities in Healthcare and Public Health

Artificial intelligence is being explored for medical imaging, clinical decision support, drug discovery, patient monitoring, health communication and public health forecasting.

Large multimodal models can work with different forms of information, including text and images, creating opportunities in healthcare and scientific research. At the same time, the World Health Organization emphasises that such systems require careful ethical and governance evaluation. WHO guidance on large multimodal models

Potential research areas include:

• AI-assisted disease detection and diagnosis
• Clinical decision-support systems
• Predictive analytics for public health
• Generative AI in medical communication
• Drug discovery and biomedical research
• Personalised treatment planning
• AI-supported mental health interventions
• Remote monitoring and telemedicine
• Bias in clinical datasets
• Patient consent and data protection
• Explainability in medical AI
• AI use in low-resource healthcare settings
• Health misinformation generated or amplified by AI
• Professional accountability for AI-supported decisions

Healthcare AI research should remain sensitive to patient safety, human autonomy and the unequal availability of health data and digital infrastructure.

A model that performs well on data from one hospital or population may not provide equivalent results elsewhere. Researchers should therefore evaluate external validity, demographic representation, clinical relevance and deployment conditions.

Interdisciplinary collaboration among clinicians, public health researchers, data scientists, ethicists, legal scholars and patient communities can help ensure that health-related AI research addresses both performance and public benefit.

AI Research Opportunities in Business and Management

Businesses increasingly use artificial intelligence for forecasting, customer service, recruitment, marketing, fraud detection, supply-chain management and strategic decision-making.

For management scholars, this creates research opportunities extending well beyond productivity measurement.

Relevant topics include:

• AI-supported managerial decision-making
• Generative AI and knowledge work
• Human-AI collaboration in organisations
• Algorithmic management and employee autonomy
• AI adoption in small and medium-sized enterprises
• Artificial intelligence and organisational innovation
• Consumer trust in AI-enabled services
• Bias in automated recruitment
• AI-enabled marketing personalisation
• Governance of enterprise AI systems
• Leadership in AI-driven transformation
• Digital skills and workforce development
• AI and entrepreneurial opportunity
• Responsible AI as a corporate capability
• Environmental costs of organisational AI use

Scholars can examine whether AI changes how authority is distributed within organisations. They can also study whether employees treat algorithmic recommendations as advice, instruction or an apparently objective substitute for human judgement.

Research is needed on how organisations establish responsibility when AI-supported decisions create financial, legal or social consequences. Such investigations may combine management, psychology, information systems, labour studies, economics, ethics and law.

AI Research Opportunities in Science and Engineering

Artificial intelligence is supporting pattern recognition, simulation, materials discovery, laboratory automation, astronomical analysis, environmental monitoring and engineering design.

Potential research directions include:

• AI-supported scientific discovery
• Automated laboratory systems
• Machine learning for materials science
• Digital twins and engineering simulation
• Predictive maintenance
• AI in renewable-energy systems
• Agricultural monitoring and precision farming
• Robotics and autonomous systems
• Computer vision for environmental assessment
• AI-assisted mathematical modelling
• Research software and reproducibility
• Human oversight of autonomous engineering systems
• Energy-efficient AI architectures
• Scientific validation of AI-generated hypotheses

Researchers should clearly distinguish prediction from explanation. A model may identify a statistically useful pattern without establishing a causal relationship or providing a scientifically valid explanation.

Scientific AI research should therefore report data provenance, validation procedures, uncertainty, limitations and reproducibility measures. Where models are used to generate hypotheses, those hypotheses should be tested through appropriate scientific methods.

AI Research Opportunities in Environmental Sustainability

Artificial intelligence can support climate modelling, biodiversity monitoring, energy management, disaster prediction, agricultural planning and environmental policy. It may help researchers analyse large datasets collected from satellites, sensors, weather systems and local observations.

Promising research themes include:

• AI-supported climate-risk assessment
• Early-warning systems for floods, droughts and wildfires
• Biodiversity identification and monitoring
• Smart energy management
• Renewable-energy forecasting
• Sustainable transportation
• Precision agriculture
• Water-resource management
• Waste detection and circular-economy systems
• Environmental misinformation
• Community participation in AI-supported planning
• Environmental effects of data centres and model training

AI for sustainability also creates an important contradiction. Artificial intelligence may contribute to environmental solutions while requiring substantial energy, water and computing infrastructure.

Researchers should examine both sides of this relationship. Environmental benefits should not be assumed simply because AI is used in a sustainability project. Studies should evaluate resource consumption, infrastructure requirements, accessibility and long-term impact.

AI Research Opportunities in the Humanities

The humanities provide essential perspectives on language, history, meaning, identity, creativity and ethics. These areas are central to understanding the social effects of generative AI.

Research opportunities include:

• AI-generated literature and creative authorship
• Computational analysis of historical archives
• Digital preservation of cultural heritage
• Artificial intelligence and translation
• Representation of minority languages
• AI-generated images and cultural stereotypes
• Machine interpretation of narrative and symbolism
• Authenticity in digital art
• Copyright and creative ownership
• Human identity in automated environments
• Philosophical questions about agency and intelligence
• Cultural differences in attitudes towards AI
• Preservation of indigenous knowledge
• AI-supported language documentation

Humanities researchers can help reveal how AI systems reproduce assumptions embedded in language, images and historical records. They can also examine how concepts such as originality, authorship, memory and creativity change when machines generate human-like content.

This work may combine literary studies, history, philosophy, linguistics, cultural studies, computer science and media research.

AI Research Opportunities in Social Sciences

Artificial intelligence affects social interaction, employment, public opinion, political communication and access to opportunities. It also provides new tools for analysing large collections of text, images and behavioural data.

Priority areas include:

• Public attitudes towards artificial intelligence
• AI and social inequality
• Algorithmic discrimination
• Automation and employment
• AI-generated political communication
• Deepfakes, misinformation and public trust
• Social-media recommendation systems
• Digital surveillance
• Community responses to automation
• AI and gender representation
• Technology adoption across social groups
• Human relationships with conversational AI
• AI-mediated public services
• Digital exclusion and infrastructure inequality

Social science researchers should avoid treating people as passive recipients of technology. Communities may accept, reject, reinterpret or adapt AI systems according to their own needs and experiences.

Qualitative interviews, surveys, experiments, ethnographic research, computational social science and mixed-method designs can all contribute to this field.

AI Research Opportunities in Law, Governance and Public Policy

Governments and institutions face difficult questions about how AI should be regulated, audited and used in public decision-making.

Research topics may include:

• Accountability for automated decisions
• AI regulation and institutional governance
• Privacy and data-protection law
• Intellectual property and generative AI
• Legal responsibility for autonomous systems
• Algorithmic decision-making in public administration
• AI-supported policing and surveillance
• Automated decisions affecting welfare or employment
• Procurement standards for public-sector AI
• Cross-border data governance
• AI audits and impact assessments
• Public participation in technology policy
• Access to justice through digital tools
• International approaches to AI governance

Policy research should assess how rules operate in practice, not simply describe formal legislation. It should consider implementation capacity, public-sector skills, enforcement, institutional accountability and the experiences of affected communities.

AI-supported policy evaluation is another emerging field. The OECD notes that quantitative text analysis may enable researchers and governments to evaluate large collections of policy documents more efficiently, although methodological and governance safeguards remain necessary. OECD on AI in policy evaluation

Responsible AI as a Cross-Disciplinary Research Priority

Responsible AI should not be treated as an additional paragraph added after a system has been developed. It should shape the research question, data strategy, model design, validation, implementation and reporting process.

UNESCO’s Recommendation on the Ethics of Artificial Intelligence centres human rights and human dignity while emphasising transparency, fairness, environmental sustainability and human oversight. UNESCO Recommendation on the Ethics of Artificial Intelligence

Researchers should consider:

• Whether the data adequately represent affected populations
• Whether the system may produce discriminatory outcomes
• Whether outputs can be explained and challenged
• Who remains accountable for decisions
• How privacy and confidentiality are protected
• Whether human oversight is meaningful
• How risks are monitored after deployment
• Whether the system is accessible to different communities
• What environmental resources the system requires
• Whether the claimed benefit is supported by evidence

The NIST AI Risk Management Framework offers a voluntary structure for incorporating trustworthiness considerations into the design, development, use and evaluation of AI systems. Its generative AI profile addresses risks specific to generative technologies. NIST AI Risk Management Framework

Researchers may use such frameworks as analytical references, but they should explain how principles were translated into concrete research procedures.

Research Methodologies for Interdisciplinary AI Studies

Interdisciplinary AI research can use a wide range of methodologies.

Quantitative Approaches

Quantitative studies may use experiments, surveys, performance evaluation, predictive modelling, statistical analysis or large-scale observational data.

Researchers should report sampling, dataset construction, model selection, evaluation metrics and uncertainty clearly. Model accuracy alone may be insufficient where fairness, safety or social outcomes are important.

Qualitative Approaches

Interviews, focus groups, ethnography, case studies and document analysis can reveal how people experience AI systems.

Qualitative methods are particularly useful for studying trust, professional judgement, organisational culture, community concerns and unintended consequences.

Mixed-Methods Approaches

Mixed-methods research can connect technical performance with human experience. A study might evaluate model accuracy quantitatively and then interview users to understand whether the system is trusted, understandable and useful.

The integration of methods should be planned from the beginning rather than performed as an afterthought.

Participatory and Community-Based Approaches

Researchers can involve affected groups in identifying problems, defining success and evaluating outcomes. Participatory methods may be valuable where AI systems influence vulnerable communities or public services.

Comparative Research

Comparative studies can examine how AI adoption differs across professions, institutions, regions, languages or regulatory environments. Such research can prevent conclusions based on one limited context from being treated as universal.

Common Weaknesses in AI Research Manuscripts

The rapid growth of AI research has also produced recurring weaknesses that authors should avoid.

These include:

• Describing an AI tool without presenting a clear research contribution
• Treating model accuracy as the only measure of success
• Using poorly documented or unrepresentative datasets
• Making causal claims from correlational evidence
• Ignoring social and institutional context
• Failing to compare results with meaningful baselines
• Overstating the generalisability of findings
• Using generative AI outputs without verification
• Omitting limitations and negative results
• Discussing ethics only in broad or symbolic terms
• Failing to disclose how AI tools were used
• Presenting speculative benefits as established outcomes

A strong paper should identify not only what an AI system can do, but also the conditions under which it was evaluated and the boundaries of the evidence.

Developing a Strong AI Research Question

Researchers should begin with a genuine disciplinary or social problem, not merely with access to an AI tool.

A useful research question may ask:

• Whether AI improves a clearly defined outcome
• How different groups experience an AI-enabled service
• Why an AI implementation succeeds or fails
• Whether a system produces unequal outcomes
• How professional judgement changes after AI adoption
• What governance mechanisms are required
• How AI affects established theories or practices
• What new methodology is needed to evaluate AI responsibly

For example, “Using generative AI in education” is a broad topic. A more researchable question might examine whether structured use of generative AI improves feedback literacy among postgraduate students without weakening independent reasoning.

Specific questions produce clearer methods, more defensible results and more useful conclusions.

Skills Scholars May Need in 2026

Not every AI researcher must become an advanced programmer. However, scholars should develop enough technical and methodological understanding to evaluate the systems involved in their work.

Important capabilities include:

• Basic AI and data literacy
• Critical evaluation of model outputs
• Understanding of bias and data quality
• Research ethics and privacy awareness
• Interdisciplinary communication
• Reproducible research practices
• Appropriate statistical and qualitative methods
• Clear documentation of AI use
• Knowledge of relevant legal and institutional requirements
• Ability to communicate limitations to non-specialists

Collaboration can help researchers combine these capabilities. A multidisciplinary team should establish shared terminology, responsibilities and quality standards at the beginning of the project.

Publishing AI Research in Research Horizons

Research Horizons provides a relevant scholarly setting for AI research that connects disciplines and addresses emerging academic or societal questions.

Potential submissions may examine AI through education, health, business, law, public policy, science, engineering, sustainability, humanities or social science. The journal is particularly suited to studies that explain why interdisciplinary collaboration was necessary and how it strengthened the investigation.

Before submission, authors should confirm that the manuscript:

• Aligns with the journal’s current aims and scope
• Presents an original and clearly defined contribution
• Describes the methodology transparently
• Explains the role of artificial intelligence
• Reports ethical safeguards where relevant
• Acknowledges uncertainty and limitations
• Uses accurate and verifiable references
• Discloses the use of generative AI tools as required
• Communicates findings to a multidisciplinary readership
• Follows the current author guidelines

Authors should use the official Research Horizons manuscript submission system and consult the journal website for current policies, manuscript categories and formatting requirements.

Digital Infrastructure for Emerging Interdisciplinary Journals

AI and interdisciplinary research place new demands on scholarly publishing. Journals need structured manuscript submission, reviewer coordination, clear author communication, article metadata and reliable online publication.

Research Horizons is powered by ScholarJMS, supporting its journal website, manuscript submission and editorial workflow.

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Frequently Asked Questions

What are the most promising AI research areas in 2026?

Promising areas include AI in education, healthcare, scientific discovery, environmental sustainability, business, public administration, law, language, cultural research and responsible AI governance.

Can researchers without a computer science background study artificial intelligence?

Yes. Researchers from other disciplines can investigate the social, professional, ethical, cultural, legal or organisational effects of AI. Collaboration with technical specialists may be valuable when the research requires model development or detailed performance evaluation.

What makes an AI study interdisciplinary?

An AI study becomes interdisciplinary when it meaningfully integrates concepts, methods or evidence from more than one field. Merely mentioning multiple disciplines is insufficient.

Can AI be used as a research tool?

Yes. AI can support data analysis, classification, translation, modelling, literature exploration and pattern detection. Researchers remain responsible for validating outputs, protecting data and reporting their methodology transparently.

Can generative AI be listed as an author?

AI systems should not be treated as authors because they cannot accept responsibility for the accuracy, integrity and ethical obligations attached to authorship.

Should researchers disclose their use of generative AI?

Researchers should follow the journal’s current policy and disclose material use of generative AI where required. Authors remain responsible for every claim, citation, analysis and conclusion.

How can researchers reduce bias in AI studies?

Researchers should examine data representation, model performance across relevant groups, measurement choices and deployment conditions. Bias evaluation should be connected to the people and contexts affected by the system.

Is model accuracy enough to demonstrate that an AI system is useful?

No. Usefulness may also depend on reliability, fairness, explainability, safety, accessibility, cost, professional acceptance and real-world implementation.

What ethical issues should AI researchers consider?

Key issues include privacy, consent, bias, transparency, accountability, human oversight, security, environmental impact and unequal access to technological benefits.

Can Research Horizons publish AI research from a single discipline?

A discipline-specific AI study may be suitable if it aligns with the journal’s current scope, demonstrates strong scholarly quality and explains its wider relevance.

Are qualitative AI studies valuable?

Yes. Qualitative studies can reveal how individuals, professionals and communities experience AI. They can investigate trust, values, institutional culture and consequences that technical metrics may not capture.

How can scholars submit AI research to Research Horizons?

Authors should review the journal’s Aims and Scope, Author Guidelines and publication policies, then submit their manuscript through the official online submission system.

Conclusion

Artificial intelligence research in 2026 is not confined to designing algorithms. It involves understanding how intelligent systems influence knowledge, institutions, professional work, culture, human rights and social development.

The strongest research opportunities are increasingly located at disciplinary intersections. Education researchers can work with computer scientists and psychologists. Medical researchers can collaborate with data scientists and ethicists. Environmental scholars can connect AI modelling with policy and community knowledge. Humanities researchers can examine language, creativity and cultural representation. Legal and governance scholars can investigate accountability and public protection.

Research Horizons encourages scholars to pursue these connections with intellectual ambition and methodological care. AI research should produce more than technological novelty. It should generate evidence that is scientifically credible, socially relevant and ethically responsible.

Scholars preparing manuscripts for 2026 are invited to explore how artificial intelligence can illuminate important research problems across disciplines while remaining attentive to human values, research integrity and the limits of automated systems.