AI-Powered Educational Agents: Opportunities, Innovations, and Ethical Challenges
Abstract
:1. Introduction
- An evidence-based taxonomy that links specific technical choices (e.g., RAG, fine-tuning, multi-agent systems) to instructional objectives (e.g., scaffolding, assessment, engagement).
- A comparative analysis of reported efficacy—quantifying, for example, how retrieval grounding reduces factual errors or how debate architectures boost accuracy on ill-structured tasks.
- A set of design principles and ethical safeguards (privacy controls, bias audits, integrity guardrails) to guide practitioners in building reliable, equitable educational agents.
1.1. Retrieval-Augmented Generation (RAG)
1.2. Prompt Engineering
1.3. In-Context Learning
1.4. Few-Shot Prompting
1.5. Dynamic Role Setting
1.6. Fine-Tuning
1.7. Multi-Agent Systems
1.8. Pedagogical Strategies
2. Methodology
2.1. Phase 1: Planning
- How can agents based on large language models (LLMs) enhance personalization, real-time feedback, and natural interaction in educational settings?
- What strategies and agentic tools can maximize the effectiveness of adaptive teaching and tutoring systems?
- What ethical challenges do educators face in the context of AI use?
2.2. Phase 2: Selection
- Articles published between 2023 and 2025.
- Articles written in English.
- Articles whose topics matched the search terms and research questions.
- Empirical primary studies (e.g., experimental, quasi-experimental, survey-based) that reported original data on LLM-based agents in education.
- Articles published before 2023, to focus on recent advancements.
- Articles not available in English.
- Articles not directly related to the search terms aligned with the research questions.
- Other academic works, including theses, technical reports, and briefs.
2.3. Phase 3: Extraction
- Technical and Pedagogical Frameworks
- LLM-Powered Tutoring Systems and Virtual Assistants
- Assessment and Feedback with LLM Agents
- Curriculum Design and Content Creation
- Personalized and Engaging Learning Experiences
- Ethical Considerations and Challenges
3. Results
3.1. Technical and Pedagogical Frameworks
3.1.1. Integrating Technical and Pedagogical Layers
3.1.2. Design Principles for LLM-Based Educational Agents
3.2. LLM-Powered Tutoring Systems and Virtual Assistants
3.3. Assessment and Feedback with LLM Agents
3.4. Curriculum Design and Content Creation
3.5. Personalized and Engaging Learning Experiences
3.6. Ethical Considerations and Challenges
3.6.1. Accuracy and Hallucination
3.6.2. Bias and Fairness
3.6.3. Privacy and Data Protection
3.6.4. Academic Integrity
- Socratic tutoring guardrails. Open-source systems such as CodeHelp refuse to reveal full solutions and instead guide students through hints and questions [17]. Similar “Socratic bots” have been deployed in data science courses with positive student ratings and minimal leakage of answers.
- Provenance technologies. Statistical watermarking of LLM output [124] and transcript-level provenance logs gives instructors post hoc tools to prove AI overuse.
4. Discussion
5. Conclusions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Strategies for Enhancing AI Tutoring Systems | Description |
---|---|
Retrieval-Augmented Generation | Connects AI with knowledge bases to ensure accurate, contextually relevant responses. |
Prompt Engineering | Shapes AI behavior using specific instructions and guardrails. |
In-Context Learning | Adapts the model’s behavior on-the-fly by leveraging examples or instructions contained within the current prompt. |
Few-Shot Prompting | Steers the model with a handful of input–output demonstrations, eliciting task-specific, well-structured responses. |
Dynamic Role Setting | Assigns (and can switch) clear personas—e.g., tutor, peer, coach—to align tone, depth, and feedback with each learner’s needs. |
Fine-Tuning | Adapts AI models to specialize in domain-specific tasks, improving performance. |
Multi-Agents Systems | Uses multiple AI agents to teach or critique each other, enhancing accuracy. |
Pedagogical Strategies | Integrates educational theories to support student learning and reflection. |
Academic Database | Keywords |
---|---|
Google Scholar | (intitle:“large language model*” OR intitle:“LLM*”) OR intitle:“agent*” AND intitle:“education” |
Scopus | TITLE-ABS-KEY ((“Large Language Models*” OR “LLM*”) AND (“agent*”) AND (“education”)) AND PUBYEAR > 2022 AND PUBYEAR < 2026 |
Category | Publication Year | Authors | Research Title |
---|---|---|---|
1. Technical and Pedagogical Frameworks | 2025 | Yusuf et al. [11] | Pedagogical AI conversational agents in higher education: a conceptual framework and survey of the state of the art. |
2025 | Kim et al. [51] | Assessing the Current Limitations of Large Language Models in Advancing Health Care Education. | |
2025 | Nikolovski et al. [52] | Advancing AI in Higher Education: A Comparative Study of Large Language Model-Based Agents for Exam Question Generation, Improvement, and Evaluation. | |
2025 | Li et al. [53] | ARCHED: A Human-Centered Framework for Transparent, Responsible, and Collaborative AI-Assisted Instructional Design. | |
2025 | Zhang et al. [54] | EduPlanner: LLM-Based Multi-Agent Systems for Customized and Intelligent Instructional Design. | |
2024 | Yu et al. [55] | From mooc to maic: Reshaping online teaching and learning through llm-driven agents. | |
2024 | Shen et al. [56] | Large Language Models for Education: A Survey and Outlook. | |
2024 | Wang et al. [57] | Enhancement of the performance of large 849 language models in diabetes education through retrieval-augmented generation: comparative study. | |
2023 | Liffiton et al. [17] | Codehelp: Using large language models with guardrails for scalable support in programming classes. | |
2023 | Eggmann et al. [58] | Implications of large language models such as ChatGPT for dental medicine. | |
2. LLM-Powered Tutoring Systems and Virtual Assistants | 2025 | Gao et al. [59] | Agent4Edu: Generating Learner Response Data by Generative Agents for Intelligent Education Systems. |
2025 | Wang et al. [60] | CyberMentor: AI Powered Learning Tool Platform to Address Diverse Student Needs in Cybersecurity Education. | |
2024 | Jin et al. [23] | Teach ai how to code: Using large language models as teachable agents for programming education. | |
2024 | Marouf et al. [61] | Enhancing Education with Artificial Intelligence: The Role of Intelligent Tutoring Systems. | |
2024 | Alsafari et al. [62] | Towards effective teaching assistants: From intent-based chatbots to LLM-powered teaching assistants. | |
2024 | Neira-Maldonado et al. [63] | Intelligent educational agent for education support using long language models through Langchain. | |
2024 | Maiti and Goel [64] | How Do Students Interact with an LLM-powered Virtual Teaching Assistant in Different Educational Settings?. | |
2024 | Molina et al. [65] | Leveraging LLM Tutoring Systems for Non-Native English Speakers in Introductory CS Courses. | |
2024 | Neumann et al. [66] | An llm-driven chatbot in higher education for databases and information systems. | |
2024 | Pan et al. [67] | ELLMA-T: an Embodied LLM-agent for Supporting English Language Learning in Social VR. | |
2024 | Gold and Geng [68] | Tracking the Evolution of Student Interactions with an LLM-Powered Tutor. | |
2024 | Gold and Geng [69] | On the Helpfulness of a Zero-Shot Socratic Tutor. | |
2024 | Henley et al. [70] | CodeAid: Evaluating a Classroom Deployment of an LLM-based Programming Assistant that Balances Student and Educator Needs. | |
2024 | Nagarajan [71] | AI-Enabled E-Learning Systems: A Systematic Literature | |
2023 | Sahlman et al. [5] | Revolutionizing Learning with GenAI. | |
2023 | OpenAI [6] | Filling Crucial Language Learning Gaps: Duolingo and GPT-4. | |
3. Assessment and Feedback with LLM Agents | 2025 | Yeung et al. [72] | A Zero-Shot LLM Framework for Automatic Assignment Grading in Higher Education. |
2024 | Lagakis and Demetriadis [73] | EvaAI: A Multi-agent Framework Leveraging Large Language Models for Enhanced Automated Grading. | |
2024 | Pardos and Bhandari [74] | ChatGPT-Generated Help Produces Learning Gains Equivalent to Human Tutor-Authored Help on Mathematics Skills. | |
2024 | Doughty et al. [75] | ChatGPT for good? On opportunities and challenges of large language models for education. | |
2023 | Chan et al. [76] | ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate. | |
2023 | Pankiewicz and Baker [77] | Large Language Models (GPT) for Automating Feedback on Programming Assignments. | |
2023 | Kasneci et al. [1] | ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate. | |
2023 | Lex [78] | Introducing Q-Chat, the World’s First AI Tutor Built with OpenAI’s ChatGPT. | |
4. Curriculum Design and Content Creation | 2025 | Tan et al. [19] | ELF: Educational LLM Framework of Improving and Evaluating AI Generated Content for Classroom Teaching. |
2025 | Mustofa et al. [79] | Integration of Artificial Intelligence (ChatGPT) into Science Teaching and Learning. | |
2025 | Durak et al. [80] | Comparison of Human-Written Versus AI-Generated Text in Discussions at Educational Settings: Investigating Features for ChatGPT, Gemini and BingAI. | |
2024 | Fleischhauer and Friedrich [81] | Factors determining the efficacy of AI-generated word problems for content-specific math language courses in higher education. | |
2024 | Luo et al. [82] | BPE: Exploring the Prompt Framework of Physics Exercises Generated from Bloom’s Taxonomy in LLM. | |
2024 | Hu et al. [83] | Teaching Plan Generation and Evaluation with GPT-4: Unleashing the 908
Potential of LLM in Instructional Design. | |
2024 | Lin and Sun [84] | A Smart English Learning Curriculum Generation Mobile Platform based on Word Root Extension using Artificial Intelligence and LLM (Large Language Model.) | |
2024 | Robinson [85] | Harnessing AI for Structured Learning: The Case for Objective-Driven Design in E-Learning. | |
2024 | Amirjalili et al. [86] | Exploring the boundaries of authorship: a comparative analysis of AI-generated text and human academic writing in English literature. | |
2023 | Denny et al. [87] | Can we trust AI-generated educational content? comparative analysis of human and AI-generated learning resources. | |
2023 | C. et al. [88] | Inculcating Digital Contents, Technological Tools: Agents for Enhancing Business Education Curriculum for Skill Acquisition in Nigerian Universities. | |
5. Personalized and Engaging Learning Experiences | 2025 | Hao et al. [21] | Student Engagement in Collaborative Learning with AI Agents in an LLM-Empowered Learning Environment: A Cluster Analysis. |
2025 | Li et al. [89] | TutorLLM: Customizing Learning Recommendations with Knowledge Tracing and Retrieval-Augmented Generation. | |
2025 | Wang et al. [90] | LearnMate: Enhancing Online Education with LLM-Powered Personalized Learning Plans and Support. | |
2025 | Joe and Sebastin [91] | Human-Ai Collaboration In Education Using Leveraging Deep Learning And Natural Language Processing To Enhance Personalized Learning Systems. | |
2024 | Damasceno et al. [92] | DALverse: Assistive Technology for Inclusion of People with Disabilities in Distance Education through a Metaverse-Based Environment. | |
2024 | Choshi [93] | Addressing Challenges in Undergraduate Community Health Nursing Clinical: Kolb’s Experiential Learning Theory. | |
2024 | Choi [94] | The Effect of Early Childhood Science Education Class Based on Kolb’s Experiential Learning Theory on The Pre-service Early Childhood Teacher’s Science Knowledge, Science Attitude, and Science Teaching Efficacy. | |
2024 | Haritha and Rao [95] | A Holistic Approach to Professional Development: Integrating Kolb’s Experiential Learning Theory for Soft Skills Mastery. | |
2024 | Sajja et al. [96] | Artificial Intelligence-Enabled Intelligent Assistant for Personalized and Adaptive Learning in Higher Education. | |
2024 | Gunawan and Wiputra [97] | Exploring the Impact of Generative AI on Personalized Learning in Higher Education. | |
2024 | Muhammad and Orji [98] | Revolutionizing Education In The Digital Era: The Role Of Ai In Promoting Inclusivity, Equality, And Ethical Innovation. | |
2024 | Bhatnagar and Sharma [99] | Using AI to Create More Equitable and Inclusive Higher Education Learning Systems. | |
2024 | Qushwa and Onia [100] | AI Innovation in Education: Realizing Personalized Learning in the Digital Age. | |
2023 | Lawrence et al. [101] | How teachers conceptualise shared control with an AI co-orchestration tool: A multiyear teacher-centred design process. | |
2023 | Echeverría et al. [102] | Designing Hybrid Human–AI Orchestration Tools for Individual and Collaborative Activities: A Technology Probe Study. | |
2023 | Kwon and Shim [103] | The study on categorizing scaffolding types of AI chatbots for primary English learning-oriented assessment. | |
6. Ethical Considerations and Challenges | 2025 | Rowsell [104] | Nine in 10 UK Undergraduates Now Using AI in Assessments – Survey. |
2025 | Microsoft Presidio [105] | Presidio: Data Protection and De-identification SDK. | |
2025 | Chu et al. [106] | Llm agents for education: Advances and applications. | |
2025 | Kumar et al. [107] | Detecting and Mitigating Bias in LLMs through Knowledge Graph-Augmented Training. | |
2025 | Xu et al. [108] | Mitigating Social Bias in Large Language Models: A Multi-Objective Approach Within a Multi-Agent Framework. | |
2024 | Dey et al. [109] | Better to Ask in English: Evaluation of Large Language Models on English, Low-Resource and Cross-Lingual Settings. | |
2024 | Yong et al. [110] | Low-Resource Languages Jailbreak GPT-4. | |
2024 | Hussain [111] | Forming Policies for Ethical AI Use in Academic Writing. | |
2024 | Cheng et al. [112] | Reinforcement Learning from Multi-role Debates as Feedback for Bias Mitigation in LLMs. | |
2024 | Echterhoff et al. [113] | Cognitive bias in decision-making with LLMs. | |
2024 | Raza et al. [114] | Mbias: Mitigating bias in large language models while retaining context. | |
2024 | Xie et al. [115] | Differentially private synthetic data via foundation model apis 2: Text. | |
2024 | Sun et al. [116] | Empowering Users in Digital Privacy Management through Interactive LLM-Based Agents. | |
2024 | Grobler [117] | Exploring Essential Concepts In The Automation Of Student Plagiarism Management-A Case Study. | |
2024 | Davies [118] | Artificial Intelligent Branching Simulations (AIBS) in Critical Care Neonatal Nursing. | |
2023 | Khan Academy [119] | Introducing Khanmigo: An AI–Powered Guide Powered by GPT-4. | |
2023 | OpenAI [120] | ChatGPT Outage: Incident Report. | |
2023 | Zou et al. [121] | Universal and Transferable Adversarial Attacks on Aligned Language Models. | |
2023 | Muscanell and Robert [122] | EDUCAUSE QuickPoll Results: Did ChatGPT Write This Report? | |
2023 | Williams [123] | Turnitin Says One in 10 University Essays Are Partly AI-Written. | |
2023 | Kirchenbauer et al. [124] | A Watermark for Large Language Models. |
Pedagogical Function | Description |
---|---|
FAQ and knowledge base | Provides on-demand answers and feedback drawn from a curated knowledge repository. |
Motivation and inspiration | Supplies emotional or cognitive cues that boost learners’ motivation, engagement, and self-efficacy. |
Administration and management | Automates routine admin and communication tasks (e.g., onboarding, scheduling), easing staff workload. |
Reflective and metacognitive skills | Promotes analytical, critical, divergent, and creative thinking to deepen self-reflection and learning. |
Simulation and experience | Creates safe simulated contexts (including affective interactions) for experiential practice. |
Assessments | Generates formative or summative feedback and feed-forward guidance on learners’ work. |
Mentorship and coaching | Monitors learner progress and delivers personalized guidance, recommendations, or instruction. |
Technological Facet | Description |
---|---|
Natural language processing | Native or API-based NLP modules that parse and generate human language for understanding user intent and producing responses. |
Connection to external data sources | Ability to query SQL databases, knowledge libraries, or other repositories in real time, enriching dialogue with up-to-date information. |
Interface with social media/IM | Agent delivered through messaging platforms (e.g., WhatsApp, Facebook Messenger), using the chat UI as the main interaction channel. |
Personalized development | Custom or otherwise unspecified codebases tailored to a specific use-case when off-the-shelf tools are insufficient. |
Communication support | Multimodal interfaces such as voice bots, virtual humans, or avatars that add speech, audio, or embodied presentation layers. |
Personalization functions | Machine-learning augmentations—eye-tracking, emotion or gaze detection, biometrics—that adapt dialogue flow to each user. |
Aspect | Description | Example | Key Findings |
---|---|---|---|
Automated Grading | Zero-shot grading of student submissions via prompt engineering, without additional model training. | AAG system prompts for scoring quantitative and open-ended answers [72] | High consistency with human grades; boosted student motivation and preparedness [72,74] |
Multi-Agent Evaluation | Multiple LLMs debate or peer-review each other’s assessments to reduce single-model bias. | ChatEval’s debate framework for open-ended responses [76] | Improved correlation with human evaluations and lower error rates [76] |
Adaptive Feedback Generation | Real-time, personalized hints and comments tailored to each student’s answer. | ChatGPT generated step-by-step algebra hints [74] | Equivalent learning gains to human-written hints, at 20× faster throughput [74] |
Automated Essay Scoring | Rubric-guided essay evaluation using LLMs with in-prompt rubrics. | GPT-4 grading essays against instructor-provided rubrics [1] | Moderate-to-high agreement with human raters when given clear rubrics [74] |
Question Generation | AI-driven creation of multiple-choice and practice questions for formative use. | GPT-4 MCQ generation in programming courses [75,78] | Expert ratings found AI-generated items on par with human-authored questions [75] |
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Córdova-Esparza, D.-M. AI-Powered Educational Agents: Opportunities, Innovations, and Ethical Challenges. Information 2025, 16, 469. https://doi.org/10.3390/info16060469
Córdova-Esparza D-M. AI-Powered Educational Agents: Opportunities, Innovations, and Ethical Challenges. Information. 2025; 16(6):469. https://doi.org/10.3390/info16060469
Chicago/Turabian StyleCórdova-Esparza, Diana-Margarita. 2025. "AI-Powered Educational Agents: Opportunities, Innovations, and Ethical Challenges" Information 16, no. 6: 469. https://doi.org/10.3390/info16060469
APA StyleCórdova-Esparza, D.-M. (2025). AI-Powered Educational Agents: Opportunities, Innovations, and Ethical Challenges. Information, 16(6), 469. https://doi.org/10.3390/info16060469