LLM-Assisted Mentoring System

• LLM-assisted mentoring systems are human-centered AI platforms that integrate large language models with structured protocols to support learning, reflection, and personalized guidance.
• They employ diverse architectural patterns—from dual feedback loops to multi-agent specialization—to tailor interventions in areas such as peer review, online learning, and creative problem solving.
• Empirical evidence shows improved learner performance and engagement despite challenges like overreliance, highlighting the importance of human oversight and adaptive learner modeling.

An LLM-assisted mentoring system is a human-centered AI system that uses LLMs to support learning, reflection, planning, practice, and feedback while preserving human judgment, learner agency, and domain-grounded oversight. Across peer review, online learning, social-skills rehearsal, teacher professional development, software-engineering methodology, entrepreneurship coaching, design feedback, programming support, and creative problem solving, the recurring design move is to treat the LLM not as an autonomous substitute for mentors, reviewers, or instructors, but as an educational assistant, mediator, coach, or collaborator embedded in structured workflows, curated knowledge sources, learner models, and human supervision (Yun et al., 14 Jan 2026, Ahn et al., 27 Jan 2026, Huang et al., 14 Aug 2025).

1. Definition, scope, and recurring use cases

The literature uses the term in a broad but consistent sense. In some systems, the LLM mentors novice peer reviewers through staged training and simulated practice rather than writing reviews in their place (Yun et al., 14 Jan 2026). In others, it mediates “conversational explainability” for students around learning recommendations, with a human mentor available through group chat when the chatbot exceeds its bounded task scope (Abu-Rasheed et al., 2024). Elsewhere, the LLM supports scenario rehearsal, personalized learning plans, curriculum-grounded feedback, software-engineering SLR planning, entrepreneurship coaching, or creative problem solving (Guevarra et al., 16 Jan 2025, Wang et al., 17 Mar 2025, Zhao et al., 6 Jul 2025, Gil-Pereira et al., 5 Jun 2026, Huang et al., 14 Aug 2025, Zha et al., 2024).

These systems differ in domain, but they converge on a common functional definition: mentoring is not merely answering questions. It includes diagnosing current understanding, selecting pedagogical moves, structuring task progression, surfacing omissions and misconceptions, supporting reflection, and helping humans prepare for or improve human-human interaction. This suggests that “LLM-assisted mentoring system” is best treated as an umbrella class of systems for developmental support rather than a single interface pattern.

System or domain	Primary mentoring function	Key structural choice
Peer review (Yun et al., 14 Jan 2026)	reviewer development	“Guided Recognition,” “Review Refinement Practice,” “Full Simulation”
Conversational explainability (Abu-Rasheed et al., 2024)	student understanding of recommendations	KG-grounded chatbot mediator with mentor fallback
GLOSS (Guevarra et al., 16 Jan 2025)	social-skills rehearsal	narrative graph with immediate and delayed feedback
LearnMate (Wang et al., 17 Mar 2025)	planning and study support	goals/time/pace/path with calendar-based plans
LearnLens (Zhao et al., 6 Jul 2025)	curriculum-grounded feedback	error-aware assessment, “Chain-of-Concept,” educator-in-the-loop
IntelliCode (David et al., 21 Dec 2025)	long-term tutoring	centralized, versioned learner state with StateGraph Orchestrator

2. Architectural patterns

A notable property of the literature is architectural heterogeneity under a stable set of design principles. Some systems are dual architectures. The peer-review position paper proposes an LLM-assisted mentoring system as the upstream educational component and an LLM-assisted feedback system as the downstream execution-support component, explicitly separating long-term reviewer development from draft-review refinement (Yun et al., 14 Jan 2026). Other systems are hybrid conversational stacks in which an interface layer, an orchestration layer, grounding sources, and escalation mechanisms are all explicit. The recommendation-support chatbot inside an Angular web app uses a dialogue manager, intent classifier, context builder, knowledge graph retrieval, prompt construction, and session management for mentor escalation; when needed, student, mentor, and chatbot enter the same group-chat session (Abu-Rasheed et al., 2024).

A second pattern is scenario-backed mentoring rather than unconstrained chat. GLOSS organizes rehearsal through a front-end builder, a narrative graph, a conversational simulator with feedback, and an analysis tool. Instructors can use a pre-built template with scripted dialogue, create a scenario from scratch, generate a scenario using a prompt to an LLM, or combine these methods, while the system can add “new transitions” to the graph when a learner response does not fit existing branches (Guevarra et al., 16 Jan 2025). Tutorly similarly converts video-based learning into a controlled apprenticeship loop by segmenting transcript content by learning goals, summarizing knowledge, selecting a pedagogical move, and realizing multi-turn interaction through a DSL (Li et al., 2024). Mentigo adopts a parallel idea for creative problem solving through a Database, a Controller Agent, and a Mentor Agent, where stage decision, state determination, and strategy selection precede surface response generation (Zha et al., 2024).

A third pattern is multi-agent specialization. I-VIP uses a Filter, Judge(s), Responder(s), and Facilitator to support knowledge expectation analysis, response scoring and classification, and feedback generation in mathematics teacher professional development (Yang et al., 5 Jul 2025). ITAS decomposes tutoring into specialist Video, Code, and Guidance agents followed by a Synthesizer, with a separate autograder and a distinct instructor-facing feedback layer (Elhaimeur et al., 27 Apr 2026). IntelliCode goes further by combining six specialized agents with a StateGraph Orchestrator under a single-writer policy, so that every pedagogical decision is mediated by a centralized, versioned learner state (David et al., 21 Dec 2025).

A fourth pattern is mentor-governed domain models. In entrepreneurship coaching, the system combines a project model and a risk model, both inspectable and editable by mentors; the LLM then performs project information extraction, risk diagnosis, reflection question generation, strategy suggestion, and agenda synthesis over those structured knowledge sources (Huang et al., 14 Aug 2025). This suggests that in mentoring systems for ill-defined domains, the decisive architectural choice is often not the base model, but whether mentoring logic is externalized into editable pedagogical structures.

3. Pedagogical mechanisms and interaction patterns

The literature repeatedly rejects a purely answer-giving role. The peer-review position paper is explicit that reviewing is an “expert judgment and reviewer development problem,” not a text-generation problem, and therefore organizes mentoring as a curriculum: learners first recognize quality, then revise flawed drafts, then write full simulated reviews and receive a principle-based mentoring report grounded in Fidelity, Clarity, Fairness, Proportionality, and Constructiveness (Yun et al., 14 Jan 2026). The same developmental logic appears in other domains.

One major family of systems derives its pedagogy from Cognitive Apprenticeship. “From Answer Givers to Design Mentors” operationalizes modeling, coaching, scaffolding, articulation, reflection, and exploration through a staged interaction protocol. The design mentor first clarifies goals and scope, then diagnoses the current design and discusses potential approaches, and finally prompts reflection and exploration. The paper’s core claim is that the crucial design problem is interaction structure: ordinary LLMs overproduce modeling and underproduce scaffolding, articulation, and reflection (Ahn et al., 27 Jan 2026). Tutorly instantiates the same six CogApp methods for programming-video learning, using different actions for declarative and procedural knowledge, and uses a DSL to sequence modeling, coaching, scaffolding, articulation, and reflection over notebook-based learning-by-doing (Li et al., 2024).

A second family emphasizes staged rehearsal and feedback loops. GLOSS supports rehearsal through branching scenarios, typed or spoken input, dynamic avatar responses, a separate prompt for immediate feedback, and an analysis tool for delayed reflection (Guevarra et al., 16 Jan 2025). Mentigo organizes creative problem solving into six CPS stages—Problem Discovery, Information Collection, Problem Definition, Solution Ideation, Solution Evaluation, and Solution Implementation—and then maps 23 student states to 20 mentoring strategies spanning task management, creativity, deep thinking, information integration, and emotional support (Zha et al., 2024). In both cases, mentoring is process management plus adaptive intervention.

A third family emphasizes methodological reasoning rather than answer provision. SLRMentor separates Mentor Chat, Search String Chat, and Criteria Chat, so that novice software-engineering researchers can ask about SLR process, construct search strings, and reason about inclusion and exclusion criteria with explanations grounded in established SLR guidelines (Gil-Pereira et al., 5 Jun 2026). The system is explicitly framed as a “beginner-friendly scaffold.” This suggests that mentoring is especially valuable when learners must translate ill-defined intentions into formal artifacts under methodological constraints.

Across these systems, a common mechanism is gradual withdrawal of support. Scaffolding appears first as clarification, hints, or structured prompts; later, the learner is asked to justify, revise, compare, or plan. The design implication is that an LLM-assisted mentoring system is pedagogically strongest when it treats generation as one move inside a larger instructional protocol.

4. Personalization, learner modeling, and grounding

Personalization in the literature ranges from lightweight context conditioning to persistent, versioned learner models. At the lightweight end, conversational explainability systems use a human-curated knowledge graph to regulate output, with prompt context divided into “the roles that the chatbot plays, the definitions from the domain, the rules that are to be followed in generating the explanation, and the additional content that is retrieved from the KG” (Abu-Rasheed et al., 2024). LearnMate uses the four-dimensional personalization framework of goals, time, pace, and path to generate plans and then converts them into machine-readable schedules and an editable calendar (Wang et al., 17 Mar 2025). SAMCares grounds responses in SHSU course materials through RAG and limits context to institutional notes plus uploaded study materials, aiming for “real-time, context-aware, and adaptive educational support” (Faruqui et al., 2024).

At a more structured level, several systems maintain explicit pedagogical state. LearnLens represents assessment through curriculum-aligned concepts and weights,

$\mathcal{C}_i=\{(c_k,w_k)\}_{k=1}^{K_i},$

and computes a concept-based score

$s_i = \sum_{k=1}^{K_i} w_k\, Match(c_k,\hat{a}_i),$

while storing a separate expression-quality flag

$\delta_i\in\{0,1\}$

so that language problems can be surfaced without changing the numeric grade (Zhao et al., 6 Jul 2025). Its retrieval layer is not flat similarity search, but a topic-restricted graph over curriculum labels: $$G = (V,E,\mathcal{T}),\quad E = \{(v_i,v_j)\mid \mathcal{L}(v_i)\cap\mathcal{L}(v_j)\neq\varnothing\},$$ with query-time restriction to the induced topic subgraph before FAISS ranking (Zhao et al., 6 Jul 2025). This is a strong example of curriculum-grounded mentoring logic.

At the most explicit end, IntelliCode defines the learner state as

$\mathbf{S}_t = \{m_t, r_t, e_t, p_t, M_t, v_t\},$

where the state includes mastery, review schedule, engagement, preferences, long-term memory, and versioning, and also maintains uncertainty per topic via Beta parameters $(\alpha_{t,i}, \beta_{t,i})$ (David et al., 21 Dec 2025). Mastery updates depend on correctness, difficulty, recency, hint use, and solve time, and the Progress Synthesizer uses SM-2 and forgetting-curve theory to compute review intervals (David et al., 21 Dec 2025). OnlineMate moves in a different direction by maintaining symbolic Theory-of-Mind hypotheses—Belief, Desire, Intention, Emotion, and Thought—alongside Bloom-level inference, so that peer-like agents can adapt their interaction strategies to misunderstandings, confusion, or motivation (Gao et al., 18 Sep 2025).

A plausible synthesis is that personalization in LLM-assisted mentoring currently has three dominant forms: grounding personalization through curated context, instructional personalization through explicit learner state and progress, and interaction personalization through inferred cognitive or psychological state. Systems differ mostly in how far they push each axis.

5. Human roles, governance, and safety

A central point of consensus is that mentoring systems are not designed to eliminate human responsibility. The peer-review position paper explicitly rejects autonomous reviewing, insists on reviewer autonomy, and frames mentoring as a voluntary, safe-to-fail environment that culminates in a Reviewer Certification that is “not mandatory” and “not a gatekeeping qualification” (Yun et al., 14 Jan 2026). The recommendation-support chatbot likewise embeds human fallback directly into the workflow: users can request mentor support from any session, and unresolved or out-of-scope requests are routed into a group chat with a human mentor (Abu-Rasheed et al., 2024). In entrepreneurship coaching, the LLM is positioned as collaborative infrastructure for mentor-novice interaction, while mentors retain authority to inspect transcripts, check diagnosis rationales, and edit the underlying risk framework (Huang et al., 14 Aug 2025).

A second governance pattern is educator-in-the-loop control. LearnLens gives teachers direct control over groups, quizzes, topic alignment, mark schemes, verifier scores, and feedback revision, including natural-language instructions such as “Keep the feedback concise” or “Do not include general suggestions” (Zhao et al., 6 Jul 2025). TAMIGO similarly positions LLM output as draft feedback for TAs rather than final judgment, and the broader lesson from that deployment is that constructive, balanced feedback is still unsafe if rubric alignment is imperfect or hallucinated criticism sounds authoritative (IIITD et al., 2024). ITAS addresses governance at the systems level through the Blind Instructor Problem: the tutor may accumulate richer traces of student thinking than instructors can see. Its answer is a feedback layer over pseudonymized event streams, with architectural privacy rather than prompt-level promises (Elhaimeur et al., 27 Apr 2026).

Risk patterns are remarkably stable across domains. Several papers warn about overreliance and deskilling, especially for novices who may accept AI guidance uncritically (Yun et al., 14 Jan 2026). Others note that users often dislike bounded refusals or fallback behavior even when those guardrails are intentional (Abu-Rasheed et al., 2024). Systems built around open-ended scenario generation acknowledge the unresolved tension between fully scripted and open interactions (Guevarra et al., 16 Jan 2025). ToM-based peer-companion systems expose a different tension: aligning with learner comfort is not always identical to maximizing cognitive challenge (Gao et al., 18 Sep 2025). Privacy and data governance also recur: SAMCares is locally constrained and credential-gated (Faruqui et al., 2024), the recommendation chatbot excludes user-profile data from prompts to comply with GDPR when using a third-party API (Abu-Rasheed et al., 2024), and IntelliCode explicitly mentions PII redaction and safety-aligned prompting (David et al., 21 Dec 2025).

Taken together, these systems suggest that governance in LLM-assisted mentoring depends less on one universal safety layer than on three coupled choices: bounded task scope, explicit human override, and inspectable pedagogical state.

6. Evidence, limitations, and future directions

Empirical support for LLM-assisted mentoring is mixed but increasingly concrete. Tutorly reports that learner performance improved from 61.9% to 76.6% in a within-subject study on exploratory data analysis, and the system also achieved 73.7% within a five-second margin for learning-goal-based video segmentation (Li et al., 2024). Mentigo reports significantly longer task duration, more dialogue rounds, higher word counts, stronger higher-order cognition on Analysis, Evaluation, and Creation, and knowledge gains from 4.33 to 6.67 with Cohen’s d = 0.944 in its CPS setting (Zha et al., 2024). LearnLens outperformed direct-prompt baselines on grading quality with MSE 3.190, Corr. 0.388, Acc. 0.354, and $\pm1$ Acc. 0.747, while keeping average latency at 11.39 s (Zhao et al., 6 Jul 2025). ITAS demonstrates systems viability rather than learning gains: the teaching layer handled 334 chat turns, the event store captured 10,628 events, and the feedback layer surfaced two findings the instructor acted on mid-semester (Elhaimeur et al., 27 Apr 2026).

Other evidence is more preliminary or deliberately limited. The recommendation chatbot reports an intent-classification accuracy of 88% on 182 user requests and a nine-person user study with positive ratings, but explicitly treats the system as a proof of concept (Abu-Rasheed et al., 2024). SLRMentor’s pilot involved four voluntary participants from an eight-student graduate course and supports a modest claim: the tool clarifies SLR planning, lowers initial barriers, and still requires active methodological judgment (Gil-Pereira et al., 5 Jun 2026). OnlineMate shows improved cognitive and emotional scores in simulated learning scenarios, with Cog. 5.20 and Emotion 61.66 for the full system, but it has not yet been validated on real learners (Gao et al., 18 Sep 2025). IntelliCode reports simulated-learner results, including 5.04% mean mastery gain and 89.1% task success when hints were used, but these are not classroom outcomes (David et al., 21 Dec 2025).

At the same time, a substantial portion of the literature is explicitly normative or architectural rather than validated. The peer-review mentoring proposal is a position paper, not an empirical system paper (Yun et al., 14 Jan 2026). GLOSS is architectural and “does not report a user study, classroom deployment, controlled experiment, baseline comparison, or quantitative metrics” (Guevarra et al., 16 Jan 2025). LearnMate is a prototype compared qualitatively against a single GPT-4o agent, not a deployed intervention (Wang et al., 17 Mar 2025). SAMCares is a protocol paper for a pilot randomized controlled study and reports no learning results yet (Faruqui et al., 2024).

The future directions are correspondingly consistent. Multiple papers call for richer learner models, stronger prerequisite handling, more transparent grounding, better mentor workload orchestration, and more explicit support for reflection and planning (Wang et al., 17 Mar 2025, Abu-Rasheed et al., 2024). Entrepreneurship coaching points toward multi-session transfer and graduated scaffolding so that novices eventually internalize the diagnostic process (Huang et al., 14 Aug 2025). Peer-review mentoring calls for broader coverage beyond review text quality and greater human oversight through meta-feedback from expert roles such as Area Chairs (Yun et al., 14 Jan 2026). ITAS suggests that end-to-end deployment requires not just a tutor, but operational persistence, event instrumentation, privacy-aware analytics, and instructor-facing awareness (Elhaimeur et al., 27 Apr 2026).

A plausible implication is that the next generation of LLM-assisted mentoring systems will be less defined by bigger models than by stronger state representations, more inspectable orchestration, and tighter coupling between AI support and human-human mentoring workflows.