AdaTutor: Adaptive Tutoring through Pedagogical Strategy Selection

Rajeev Bhatt Ambati; Shashank Srivastava; Snigdha Chaturvedi

cs.AIOct 26, 2026

AdaTutor: Adaptive Tutoring through Pedagogical Strategy Selection

Authors: Rajeev Bhatt Ambati, Shashank Srivastava, Snigdha Chaturvedi

Organizations: UNC Chapel Hill

Abstract

Effective tutoring requires determining not only what information to provide, but also which form of assistance to deliver. A tutor may address the same student error using various pedagogical strategies, adapting its approach as the interaction progresses. We introduce Comparative Pedagogical Supervision (CPS), a training framework to learn to select the optimal strategy and feedback for student learning. We evaluate CPS on math and science reasoning datasets. After five tutoring turns, CPS outperforms the strongest fixed-strategy baselines. The learned tutor transfers across datasets, and its strategy selection can also inform a separate API-based tutor.

Figures & tables

Figure 1: Pedagogical strategy varies according to the tutoring context: Depending on the student’s response, further support may be provided through a Worked example or a Hint for subsequent steps.

Figure 2: Overview of CPS: During training, we sample one feedback for each pedagogical strategy from the tutor. Independent student attempts measure how often each strategy leads to a correct answer. A strategy and its feedback provide supervision when its score is positive and strictly higher than others. A separate exploration policy selects the strategy used to continue the multi-turn interaction. At inference, CPS generates a ’ [strategy] feedback ’ in one call. strategy tag helps with interpretability.

Tutor	GSM8K	SVAMP	ScienceQA
Initial accuracy ( $A_{0}$ )	4.3	8.5	14.4
Hint	8.4	16.3	41.7
Step scaffold	32.6	32.8	51.8
Explicit explanation	48.9	31.3	50.0
Worked example	66.4	79.9	48.7
Self-explanation	7.3	16.5	40.6

Table 1: Final cumulative accuracy

A_{5}

(%) unless otherwise indicated. The final two rows give CPS’s gain over initial accuracy in percentage points and its correction rate (%). Bold marks the highest final accuracy and underlining identifies the strongest fixed strategy. Differences are computed before rounding.

Dataset	Knowledge gap	Misconception	Mixed
GSM8K	$88.3\rightarrow\mathbf{89.3}$	$40.5\rightarrow\mathbf{52.0}$	$63.7\rightarrow\mathbf{71.3}$
SVAMP	$\mathbf{90.9}\rightarrow 90.2$	$70.4\rightarrow\mathbf{76.5}$	$71.1\rightarrow\mathbf{74.6}$
ScienceQA	$45.6\rightarrow\mathbf{53.7}$	$47.3\rightarrow\mathbf{67.7}$	$45.9\rightarrow\mathbf{54.2}$

Table 2: Correction rates (%) by student type: Each cell shows the strongest fixed-strategy tutor followed by CPS, with the higher value in bold. Worked example is strongest across types on GSM8K and SVAMP. Contrastive feedback is strongest across types on ScienceQA. Differences are computed before rounding.

Figure 3: Correction rates for the student-type oracle and CPS. The oracle uses test outcomes to choose a fixed strategy for each student type and matches the globally strongest fixed strategy within each dataset.

Dataset	Switch Rate (%)	Sessions (%)	Cond. MI (bits)
GSM8K	62.3	79.2	0.17
SVAMP	54.3	67.3	0.08
ScienceQA	56.0	84.9	0.19

Table 3: Strategy changes during tutoring. Switch rate uses adjacent tutor-turn pairs. Sessions with a switch uses sessions that reach at least two tutor turns. Conditional mutual information measures the association between consecutive strategies after accounting for student type and turn.

Context	GSM8K	SVAMP	ScienceQA
Full history	72.7	80.2	63.9
Latest exchange	72.8	78.1	65.4
Latest student reply	72.4	79.2	65.2
Problem only	50.6	72.3	66.7

Table 4: Final accuracy

A_{5}

(%) with different amounts of dialogue context. All variants receive the problem and reference solution and are trained separately under the same checkpoint-selection rule. Bold marks the highest accuracy per dataset. Differences are computed before rounding.

Dataset	Tutor	Fixed (%)	Own (%)
GSM8K	Prompt Selector	2.9	4.7
GSM8K	CPS	37.0	39.1
SVAMP	Prompt Selector	9.2	13.8
SVAMP	CPS	43.0	46.6
ScienceQA	Prompt Selector	21.9	27.7
ScienceQA	CPS	35.6	43.5

Table 5: First-turn correction rates among initially incorrect cases. Fixed response uses feedback from the fixed-strategy tutor corresponding to the selected strategy. Own response uses each method’s generated feedback. Differences are computed before rounding.

Figure 4: Qualitative tutoring examples: (a) CPS changes from Contrastive feedback to Hint after the initial intervention does not resolve the student’s error, while the Prompt Selector continues eliciting explanation of the same incorrect step. (b) Both tutors use Step scaffold , but CPS targets the student’s remaining confusion between accumulated amount and earned interest, while the fixed-strategy tutor continues scaffolding an earlier calculation. Strategy labels are retained for analysis but are not shown to the student.

Source dataset	GSM8K	SVAMP	ScienceQA
GSM8K	72.7	78.7	49.1
SVAMP	75.5	82.5	51.1
ScienceQA	33.5	31.1	64.1
Strongest fixed strategy	66.4	79.9	54.0

Table 6: Cross-dataset final accuracy

A_{5}

(%). Rows identify the tutor’s training dataset. Columns identify the evaluation dataset. Underlining denotes in-domain evaluation, and bold marks the highest trained-tutor accuracy per target. The target’s strongest fixed strategy is shown for comparison.

Tutor	$A_{1}$	$A_{5}$	Correction (%)
Step scaffold	64.0	82.6	58.3
Contrastive feedback	72.9	82.6	58.3
Prompt Selector	68.0	87.9	70.9
CPS selection + API	73.3	89.9	75.7

Table 7: Strategy selection with GPT-4.1-mini as the response generator (%). This earlier GSM8K study uses 247 assignments with

A_{0}=58.3\%

, rather than the current main evaluation. The two strongest fixed strategies are shown. Differences are computed before rounding.

Appendix figures & tables17 assets

Supplementary material from the paper’s appendix.

Appendix

Problem: Mia has 6 marbles. Leo has twice as many. Each person receives 3 more marbles. How many marbles do they have altogether?
Initial student response: "Leo has $6+2=8$ marbles because ’twice as many’ means two more.”
Strategy	Definition	Illustrative tutor response
Minimal hint	Provide a brief cue directing attention to the relevant concept or relationship, allowing the student to independently determine the subsequent step.	"Focus on the word ’twice’ when comparing Leo’s marbles with Mia’s."
Step scaffold	Guide the next step by breaking it into smaller actions or providing a partial setup.	"First find Leo’s starting number of marbles. This is two groups of 6. What is $2\times 6$ ?"
Explicit explanation	Explain the relevant concept or rule and the reasoning behind it.	"’Twice as many’ means two equal groups of the original amount. Multiplication by two expresses this relationship."
Worked example	Demonstrate the relevant reasoning on an analogous problem with different values. Then, ask the student to apply the same method.	"Suppose Ana has 4 marbles and Ben has twice as many. Ben has $2\times 4=8$ . If each receives 1 more, their total is $(4+1)+(8+1)=14$ . Apply the same steps to Mia and Leo."

Appendix

Table 8: Definitions and examples of the six pedagogical strategies. Each row illustrates an example response of the respective strategy to the same tutoring context. All strategies withhold the final answer to the original problem.

Figure 5: Student simulation : The initial attempt uses one call for private reasoning and another for the public reply. After tutor feedback, one call updates the KC state, and another generates the reply and answer under that state. Only the public reply enters the tutor’s dialogue. The example illustrates the procedure rather than reporting an experimental result.

Figure 6: Dataset-specific KC construction: We group similar reasoning steps from training solutions, identify reusable KCs, and merge overlapping definitions. The resulting library is used to annotate problems. Incorrect beliefs are checked on problems requiring the corresponding KC before they are included in student profiles. The figure uses an illustrative example.

	GSM8K	SVAMP	ScienceQA
KCs in annotation library	34	34	TBC
KCs eligible for profile construction	28	28	TBC
Affected KCs per profile	4	4	TBC
Knowledge-gap profiles	5	5	5
Misconception profiles	5	5	5
Mixed profiles	5	5	5

Appendix

Table 9: Current test populations. An assignment pairs a student profile with an applicable problem. The same problem can be evaluated under more than one profile. Mastered controls are used for screening rather than the main tutoring comparison.

Setting	GSM8K	SVAMP	ScienceQA
Training assignments	2,934	3,000	8,624
Validation assignments	485	375	1,606
Training SFT examples	2,760	2,998	6,112
Validation SFT examples	469	378	1,111
Training epochs	3	4	4
Maximum sequence length	4,096	8,192	4,096

Appendix

Table 10: Training records for the current GSM8K, SVAMP, and ScienceQA tutors. Sequence lengths are in tokens. Assignment counts and SFT-example counts measure different units.

Generation stage	Temperature
Initial student attempt	0.0
Tutor response	0.3
Student state update	0.0
Subsequent student response	0.3
Candidate-scoring student attempt	0.3

Appendix

Table 11: Decoding temperatures by generation stage. Student state updates and public response generation are separate calls.

Setting	Value
LoRA rank	16
LoRA scaling factor	32
LoRA dropout	0.05
Learning rate	$2\times 10^{-4}$
Learning-rate schedule	Cosine decay
Warmup	5%

Appendix

Table 12: CPS fine-tuning configuration.

Figure 7: Pedagogical strategy choices by tutoring turn on GSM8K. Percentages use valid strategy tags from interactions still active at each turn.

Figure 8: Pedagogical strategy distributions by tutoring turn on SVAMP. Percentages are computed over valid strategy tags from interactions still active at each turn.

Figure 9: Pedagogical strategy distributions by tutoring turn on ScienceQA. Percentages are computed over valid strategy tags from interactions still active at each turn.

Configuration	SFT examples	Validation $A_{5}$
Outcome construction
Direct completion, $K=10$	773	88.55
One student response followed by ten probes	586	70.37
Ten student responses with paired probes	667	71.72
Trajectory continuation
$\varepsilon=0.0$	773	88.55

Appendix

Table 13: Sequential training ablations on GSM8K validation interactions. All configurations are evaluated on the same initial student responses. Results use checkpoints selected by minimum validation loss. Bold indicates the selected configuration within each comparison group.

Student reply	$N$	All KCs	Reached KCs	Full response	Answer agree
Before tutoring	270	78.1	63.7	58.1	100.0
After tutor feedback	180	81.8	76.1	61.1	97.8

Appendix

Table 14: Luna’s evaluation of student replies on GSM8K (%). All KCs and Reached KCs summarize KC-level judgments. Entire response and Answer agreement summarize response-level judgments.

N

is the number of replies, not the number of KCs.

Criterion	Luna (%)
Requested strategy appears	96.1
Requested strategy is the main approach	92.8
Mathematically correct feedback	95.0
Correct final answer disclosed	0.0

Appendix

Table 15: Luna’s evaluation of 180 fixed-strategy tutor responses. The strategy-presence and main-approach criteria distinguish including a strategy from using it as the primary assistance.

Judgment	Luna	Human majority	Exact agreement
Student replies: 50 KCs with a resolved human majority
Consistent with assigned knowledge	24/50 (48.0%)	21/50 (42.0%)	42/50 (84.0%)
Violates assigned knowledge	7/50 (14.0%)	10/50 (20.0%)
Relevant step not reached	19/50 (38.0%)	19/50 (38.0%)
Knowledge-state changes: 12 changed events
Feedback supports the change	11/12 (91.7%)	10/12 (83.3%)	11/12 (91.7%)

Appendix

Table 16: Luna and human judgments on the same items. Counts give the number assigned each label, with percentages in parentheses. Exact agreement compares the complete categorical judgments. For student replies, it compares all three labels across the 50 KCs with a human majority. The nine unresolved KCs are excluded from this comparison but retained in the rater-agreement analysis.

Criterion	Human pairwise agreement (%)	Luna–human $\kappa$
Student knowledge-state labels	41.8	0.74
Feedback supports the change	77.8	0.62
Feedback addresses the changed KC	94.4	1.0
Requested strategy is the main approach	93.3	0.83
Mathematically correct tutor feedback	86.7	0.64

Appendix

Table 17: Agreement statistics for the human studies. Unweighted Cohen’s

\kappa

compares Luna with human majorities. For student knowledge-state labels, human agreement uses all 59 KCs and the Luna comparison uses the 50 majority-resolved KCs. A dash indicates an undefined coefficient.

Feedback	Target KC changed
Relevant	40/40
Retry only	0/40
Irrelevant	0/40
Misaligned	0/40
Answer reveal	0/40

Appendix

Table 18: Recorded KC-state changes under different feedback on GSM8K. Each condition starts from the same initial response and state. Relevant feedback changes only the targeted KC. None of the four control conditions changes the student’s state.

Student state	Accuracy (%)
Before feedback	5.0
After retry only	5.0
After irrelevant feedback	5.0
After misaligned feedback	5.0
After relevant feedback	67.7
After answer reveal	5.0

Appendix

Table 19: Accuracy on 300 new GSM8K problems using the student’s state before or after feedback on a different problem. Control conditions preserve the original state and share the same evaluated answers.

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