Proactive Critical Thinking

• Proactive critical thinking is a design orientation that elicits reflective inquiry before completing tasks, reducing over-reliance on default responses.
• It involves mechanisms such as assessment redesign, reflective prompts, and anticipatory computation to enforce higher-order thinking in education and AI systems.
• By integrating self-critique, clarification seeking, and hypothesis generation, it improves decision accuracy and fosters deeper analytical engagement.

Searching arXiv for papers on proactive critical thinking and related AI-supported critical thinking systems. Proactive critical thinking is a design orientation in which critical reflection is elicited before error, over-reliance, or low-effort completion becomes the default outcome. In the literature, it appears as a shift from reactive practices—such as post hoc detection, generic refusal, or one-shot answer delivery—toward systems, prompts, assessments, and agent architectures that require or initiate analysis, evaluation, creation, clarification, counterargument, or anticipation. In education, it is framed as redesigning assessment so that success requires higher-order thinking rather than recall or summarization; in interactive systems, it is framed as prompting users to pause, inspect, justify, and compare; and in agentic LLM research, it is framed as anticipating future needs, asking for missing information, simulating hypothetical futures, or critiquing intermediate reasoning while it is being produced (Akbar, 30 Mar 2025).

1. Conceptual scope and recurring distinctions

A recurring distinction is between reactive and proactive critical thinking. In assessment design, the shift is from asking whether a student used AI to asking whether an assessment is designed so that doing it well requires genuine critical thinking that AI cannot easily substitute (Akbar, 30 Mar 2025). In conversational search, the contrast is between “Passive / reactive engagement,” such as accepting initial GenAI answers and stopping after a single query, and “Active / proactive critical engagement,” such as asking follow-up queries, verifying information via sources, looking for unexpected information, and deliberately considering missing or opposing perspectives (Singh et al., 29 May 2025). In LLM collaboration, the contrast is between “passive critical thinking,” where a model detects that a query is unanswerable and refuses, and “proactive critical thinking,” where it actively seeks the missing or clarifying information by asking targeted follow-up questions and then uses the new information to produce a correct answer (Wang et al., 31 Jul 2025).

The same distinction appears in broader tool design. ClarifAI is built around dual-system theory: System 1 is fast, automatic, associative, and low effort, whereas System 2 is slow, controlled, rule-based, effortful, and flexible; the tool is designed to move readers from quick noticing to slower, more reflective analysis (Zavolokina et al., 2024). In philosophical interviews, current LLMs are criticized for low “initiative,” defined as “a resource’s ability to set its own intentions and goals, possibly different from its user’s, and to execute actions oriented towards those intentions,” and low “selfhood,” defined by locally persistent internal states such as perspectives, beliefs, opinions, and memory; this yields the selfhood–initiative model of critical thinking tools (Ye et al., 2024). Taken together, these works suggest that proactive critical thinking is not a single method but a family of interventions that make critique, clarification, and anticipation part of the primary workflow rather than an afterthought.

2. Operational mechanisms

The literature repeatedly instantiates proactive critical thinking through a small set of mechanisms. The same mechanisms recur across assessment, tutoring, decision support, and agent architecture.

Mechanism	Typical operation	Representative papers
Assessment redesign	Shift tasks toward Analyze, Evaluate, Create	(Akbar, 30 Mar 2025)
Reflective prompts and explanations	Ask users to compare, justify, verify, and broaden perspectives	(Zavolokina et al., 2024, Singh et al., 29 May 2025)
Question-based support	Present domain-grounded questions instead of only recommendations or explanations	(Fischer et al., 28 Mar 2026, Tian et al., 10 Feb 2026)
Socratic interrogation	Force reasoning through question-only dialogue	(Favero et al., 2024)
Clarification seeking	Ask for missing information instead of guessing or refusing	(Wang et al., 31 Jul 2025)
Anticipatory computation	Use idle time or hypothetical rollouts to prepare future reasoning	(Hu et al., 25 May 2026, Wang et al., 3 Jul 2026, Sui et al., 5 Sep 2025)
Interleaved self-critique	Alternate reasoning steps with critique steps	(Xu et al., 17 Dec 2025)
Meta-reasoning scaffolds	Insert “insights” that review current status and set the next goal	(Li et al., 26 Aug 2025)

These mechanisms differ in surface form but converge on the same structural move: they reduce the probability that a human or model will treat the first answer, the default framing, or the current state as sufficient. This suggests that proactive critical thinking is best understood as a change in control structure. The system either changes the task, changes the interaction, or changes the reasoning loop so that analysis and evaluation are mandatory rather than optional.

3. Assessment design and the educational formulation

The clearest educational formulation treats proactive critical thinking as a response to generative AI’s tendency to let students bypass cognitive effort. In one study, 65% reported minimal changes to AI output, 75% believed AI answers were more accurate than their own, and 60% of surveyed students felt AI hindered their creativity. The same work reviews 12 open-access and 2 commercial AI detectors and reports that none exceeded ~80% accuracy, false positives can reach up to 50%, false negatives can reach 100% for some tools under certain conditions, about 20% of AI-generated texts are misclassified as human-written even without obfuscation, and nearly 50% of obfuscated AI texts are misidentified as human. The proposed alternative is assessment design anchored in Bloom’s revised taxonomy, where lower-order thinking maps to high AI-solvability and higher-order thinking maps to lower AI-solvability (Akbar, 30 Mar 2025).

In that formulation, proactive critical thinking is operationalized through AI-resilient assessment. Such tasks require higher-order thinking that AI cannot fully replicate, demand contextual, personal, or design-based reasoning, encourage originality and creative problem-solving, and reduce the usefulness of AI as a substitute for thinking while still allowing it as a support if used transparently. The same paper presents a web-based Python tool that extracts assignment text with PyMuPDF and combines GPT-3.5 Turbo, BERT-based semantic similarity, and TF-IDF-based surface complexity metrics. It outputs an AI-solvability percentage and a categorical label: Category 1 $>74\%$, Category 2 $65–74\%$, Category 3 $50–64\%$, and Category 4 $<50\%$. In an evaluation of 50 computer science assignments, 8 tasks scored $>74\%$, 22 scored $65–74\%$, 16 scored $50–64\%$, and only 4 were $<50\%$, indicating that most existing assessments were relatively easy for AI (Akbar, 30 Mar 2025).

The pedagogical significance of this approach lies in its workflow. Diagnosis reveals where current assessments sit on the AI-solvability and Bloom’s spectrum; reflection asks where lower-order skills are being over-assessed; redesign then shifts tasks toward analysis, evaluation, creation, justification, and process documentation. This suggests that proactive critical thinking, in education, is less a student trait than a property of instructional design. It is built into the assignment before any answer is produced.

4. Interactive systems that prompt reflection, questioning, and revision

A second major lineage operationalizes proactive critical thinking through interfaces that insert explanation, prompting, or questioning into ordinary interaction. ClarifAI exemplifies this in digital news reading. It is a browser extension, “On” by default, with real-time LLM-based detection, color-highlighted “content flags,” and tooltip explanations that name the propaganda technique, quote the passage, and explain why it is propagandistic in context. In a between-subjects experiment $(N = 248)$, propaganda awareness was 82% in the Light condition and 96% in the Full condition; Net Promoter Score was $-26$ in Light and $65–74\%$0 in Full; overall thinking mode shifted from $65–74\%$1 in Basic to $65–74\%$2 in Full; and mean reading time per article rose from $65–74\%$3 s in Basic to $65–74\%$4 s in Full. The central result is that explanations, not just flags, more consistently push users into a more System‑2‑like profile (Zavolokina et al., 2024).

In GenAI search, metacognitive prompts have been shown to alter search behavior in a similar direction. A study with $65–74\%$5 introduced five prompt categories—Orienting, Monitoring, Comprehension, Broadening perspectives, and Consolidation—and found that the cues condition produced significantly broader topical exploration $65–74\%$6 vs $65–74\%$7 topics, $65–74\%$8 and significantly higher persistent inquiry $65–74\%$9 vs $50–64\%$0, $50–64\%$1, with marginal increases in total, receptive, and critical queries (Singh et al., 29 May 2025). The mechanism is explicit: users are asked what qualities they want the answer to have, whether anything was unexpected, what they do not fully understand, what perspectives they might be missing, and what the main points are that they have learned.

Socratic tutoring applies the same principle through question-only interaction. A Socratic chatbot built on Llama2 7B and 13B uses a prompt that instructs the model to “ASK THE STUDENT ONE SHORT Socratic question” and to use only dialogue history and the new student response. It is trained on SocratiQ question types such as Clarification, Probing assumptions, Probing reasons and evidence, Probing implications and consequences, and Probing alternative viewpoints and perspectives. In simulated Theory of Knowledge dialogues, the Socratic variants substantially outperform a basic tutor and a random tutor on BLEU, ROUGE-L, METEOR, BERTScore, and an LLM-based critical-thinking score; the highest average LLM-score reported is $50–64\%$2 for Socratic Llama2 13B, versus $50–64\%$3 for the basic tutor and $50–64\%$4 for the random tutor (Favero et al., 2024).

Question-based decision support pushes the same idea into professional settings. One medical prototype layers a taxonomy of data-driven questions over a treatment DSS for chronic low back pain. The questions target model behavior and decision boundaries, assumptions, feature relevance, contextualization, uncertainty, and counterfactual possibility. In semi-structured interviews with $50–64\%$5 clinical spine experts, such questions were perceived as reminders to check information, prompts for discussions with patients, and aids for considering alternatives to the apparent best option, although experienced clinicians often found some questions redundant (Fischer et al., 28 Mar 2026). A more formal version appears in AI-Assisted Critical Thinking (AACT), where the system elicits the human’s own argument, performs counterfactual analysis of that argument, and then asks the user to reassess confidence under additions, removals, or alternatives. In a house-price case study, AACT reduced over-reliance on AI relative to a recommender baseline, but also increased mental demand and effort (Tian et al., 10 Feb 2026).

5. Proactive reasoning inside models and agents

A third lineage relocates proactive critical thinking from human-facing scaffolds into the model’s own reasoning loop. One direction uses anticipation during idle time. ProAct defines a closed loop

$50–64\%$6

where dialogue history $50–64\%$7 and persistent memory $50–64\%$8 produce candidate future needs $50–64\%$9, score them with

$<50\%$0

and then generate provenance-linked artifacts $<50\%$1 for push, queue, or storage. On ProActEval, a benchmark of 200 scenarios across 40 domains, ProAct reduced $<50\%$2 from $<50\%$3 to $<50\%$4 $<50\%$5, reduced user effort from $<50\%$6 to $<50\%$7 $<50\%$8, lowered hallucination rate from $<50\%$9 to $>74\%$0 $>74\%$1, and achieved Anticipation Recall $>74\%$2 versus $>74\%$3 for the reactive baseline (Hu et al., 25 May 2026).

A second direction uses proactive thinking during dialogue downtime. In this framework, the model precomputes a reasoning trace $>74\%$4 before the next user message arrives, then continues with

$>74\%$5

The implementation generates a small set of hypothesized future user replies, prepares rollouts for each, and then reuses the compatible prefix of the selected rollout through speculative continual thinking. Across time-aware variants of 20 Questions, AgentClinic, and IN3, proactive thinking preserves almost the same accuracy as reactive chain-of-thought while substantially lowering latency; for example, on AgentClinic with Qwen3.6‑27B, accuracy rises from $>74\%$6 in direct response to $>74\%$7 in proactive thinking, with latency $>74\%$8 s rather than $>74\%$9 s for reactive thinking (Wang et al., 3 Jul 2026).

A third direction makes proactive critical thinking explicit within the generation trace. TBYS inserts an “insight” $65–74\%$0 before each reasoning step $65–74\%$1, so the model first reviews the current status and initiates the next step, then executes it. The approach uses automatically collected and filtered in-context examples, with insight scoring

$65–74\%$2

On MATH‑500, TBYS achieves $65–74\%$3 versus $65–74\%$4 for k-shot CoT and $65–74\%$5 for skill-based CoT (Li et al., 26 Aug 2025). Stepwise Think‑Critique (STC) goes further by interleaving reasoning and self-critique inside one model,

$65–74\%$6

and optimizing a hybrid reinforcement-learning objective that combines reasoning rewards and critique-consistency rewards. On mathematical benchmarks, STC-GRPO reaches average Pass@1 $65–74\%$7 in full mode, compared with $65–74\%$8 for the base model, while also producing stepwise critique traces (Xu et al., 17 Dec 2025).

A fourth direction defines proactive critical thinking as clarification seeking. GSM‑MC and GSM‑MCE remove key variables from GSM8K problems so that success requires the model to ask for missing information. With reinforcement learning and reward shaping for asking when a problem is unanswerable, Qwen3‑1.7B improves from $65–74\%$9 to $50–64\%$0 accuracy on GSM‑MC, and the request ratio rises to $50–64\%$1; analogous gains appear on GSM‑MCE and on Llama‑3.2‑3B‑Instruct (Wang et al., 31 Jul 2025). Finally, WiA‑LLM generalizes the same anticipatory logic to game-world forecasting. Given current state $50–64\%$2 and action $50–64\%$3, it predicts structured state change $50–64\%$4 and is trained with GRPO against a rule-based reward over state-difference components. In Honor of Kings it reaches $50–64\%$5 forecasting accuracy, with especially large gains in high-difficulty scenarios (Sui et al., 5 Sep 2025).

6. Tensions, limitations, and future directions

Despite their shared ambition, proactive critical thinking systems introduce their own tensions. One is cognitive cost. AACT reduces over-reliance but also produces higher mental demand and effort (Tian et al., 10 Feb 2026). Data-driven questioning in decision support must be “not too demanding,” and its timing matters because questions can be neglected or feel like friction if introduced at the wrong moment (Fischer et al., 28 Mar 2026). In propaganda detection, highlights alone did not significantly shift overall thinking mode, while explanations increased both deliberation and reading time, suggesting that more effective critical engagement is also more effortful (Zavolokina et al., 2024). Metacognitive prompts in GenAI search were most helpful for some users and less effective for participants with limited metacognitive flexibility or high confidence in their prior knowledge (Singh et al., 29 May 2025).

A second tension is initiative versus user control. Philosophers interviewed about LLMs argue that current systems are poor critical-thinking tools because they have low selfhood and low initiative; the paper proposes three alternative roles—Interlocutor, Monitor, and Respondent—defined by different combinations of selfhood and initiative (Ye et al., 2024). This suggests that proactive critical thinking requires systems that do more than comply, but also that such systems must avoid becoming intrusive, manipulative, or overtrusted. Agentic architectures make the same trade-off in technical form: ProAct can reduce effort and hallucinations, but proactive pushes can distract users, alter the optimal conversation path, and consume substantial token budgets (Hu et al., 25 May 2026). Proactive thinking in dialogue reduces user-visible latency, but it still adds backend compute and becomes less effective when user behavior is high-entropy (Wang et al., 3 Jul 2026).

A third tension is fairness and validity. Assessment design work argues that AI-resilient tasks are “not” guaranteed “AI-proof,” that creativity and originality are difficult to capture quantitatively, and that solvability landscapes shift as GPT‑3.5 Turbo and current BERT models are replaced by stronger systems (Akbar, 30 Mar 2025). Question-generation systems in medicine can hallucinate variables not present in the case dataset, which is why retrieval-augmented generation and domain constraints are proposed (Fischer et al., 28 Mar 2026). Bias-sensitive domains raise further concerns: ClarifAI notes the risk of political bias and hallucination, and propaganda identification itself showed expert disagreement in 32% of cases (Zavolokina et al., 2024).

The future directions are correspondingly convergent. The literature repeatedly proposes adaptive and personalized interventions, integration into LMS or workflow systems, richer user modeling, longitudinal studies of habit formation, and broader domain coverage beyond the original testbed. This suggests that proactive critical thinking is becoming a general design criterion for AI systems: they should not merely answer, recommend, or detect, but actively organize the conditions under which better thinking becomes more likely (Akbar, 30 Mar 2025).