Interactive Questioning Processes

• Interactive questioning processes are structured, multi-turn dialogue systems that iteratively refine information through targeted queries and adaptive feedback.
• They employ state tracking and uncertainty evaluation methods, such as entropy reduction and reinforcement learning, to optimize inquiry.
• Applications span education, dialogue systems, active learning, and clinical assessments, effectively improving engagement and decision-making.

Interactive questioning processes are structured, multi-turn mechanisms in which agents (software, humans, or hybrid systems) elicit, generate, and refine information through targeted questioning, adaptive feedback, and iterative dialog. These processes are foundational across domains of education, dialogue systems, machine learning, human-computer interaction, and cognitive assessment. Rather than relying on one-shot exchanges, interactive questioning dynamically adapts to ambiguity, uncertainty, or informational gaps, often maximizing the informativeness or utility of each interaction. Core implementations integrate real-time data collection, model-driven question selection, user-adaptive feedback, and principled assessment of both the questions and answers.

1. Fundamental Principles and Taxonomies

Interactive questioning processes extend classical query–response paradigms by explicitly modeling the uncertainty and state within an evolving exchange. Key taxonomic distinctions include:

• Disambiguation vs. Exploration: Processes may focus on resolving query ambiguity (e.g., entity or intent clarification) or on broadening conceptual exploration (e.g., generating analogies or causal hypotheses) (Biancofiore et al., 2022).
• Human-in-the-loop vs. Fully Automated: Participants can be exclusively human, exclusively machine (agent–agent), or hybridized with human oversight, e.g., supervisor fallbacks in educational settings (Datta et al., 2021).
• Question Types and Roles: Interactive questioning involves various question archetypes—clarification, probing, creative/causal, information-seeking, or Socratic coaching—often organized via typed taxonomies or scoring rubrics (Kowalski et al., 2013, Holub et al., 21 Jan 2026).
• Dialogue Structure and State: The system may be stateless (history-free), session-based, or maintain explicit dialog memory/state (e.g., Conversational QA Systems with state trackers, persistent context, or tree memories) (Biancofiore et al., 2022, Hu et al., 15 Aug 2025).

Formalizations frequently adopt Markovian (MDP, POMDP) or Bayesian state/action frameworks, with each question–answer pair updating the dialogue state and associated probability distributions over latent knowledge, belief, or intent spaces (Yu et al., 2019, Yuan et al., 2019).

2. Methodologies for Interactive Questioning

2.1 Workflow Architectures

Interactive questioning processes are commonly implemented as explicit multi-stage workflows:

• Preparation: Selection or construction of an environment (e.g., simulation applets in physics or MDPs in risk elicitation), instrumentation for response capture (e.g., InkSurvey for in-class responses) (Kowalski et al., 2013).
• Turn-based Interactions: Iterative loops where, at each turn, an agent (learner, student, or model) decides whether to ask, answer, or wait. Question selection is adaptive, often guided by entropy reduction, information gain, or reinforcement learning objectives (Yu et al., 2019, Li et al., 2016, Cheng et al., 2023).
• Real-time Assessment & Feedback: Live coding and aggregation of questions and responses (e.g., category tagging of student queries), dashboard views for instructors or supervisors, and automated confidence/uncertainty estimation triggering human fallback (Kowalski et al., 2013, Datta et al., 2021).
• Interaction Termination: Either fixed-length or dynamically determined based on adequacy functions, explicit stopping signals (e.g., Socratic dialogue “Great question!”), or convergence criteria (Holub et al., 21 Jan 2026, Hu et al., 15 Aug 2025).

2.2 Question Selection and Generation

• Information-Theoretic Selection: Maximizing expected entropy reduction or information gain over the state/belief distribution when choosing the next question (e.g., “select q maximizing IG(y; q | history)”) (Yu et al., 2019, Zafar et al., 2020, Cheng et al., 2023).
• Learned or Heuristic Policies: RL-trained policies balance the cost (cognitive, annotation, or time) of asking against the value of new information; model confidence thresholds and learned oracle-selection heads are also widely used (Kiyasseh et al., 2020, Li et al., 2016).
• Taxonomy-Driven or Role-Specialized Generation: Category-driven question generation, e.g., six curiosity types (incongruous, congruous, modifying, generalizing, causal, informational), IQA rubrics, or Socratic coach questions in multi-agent setups (Kowalski et al., 2013, Datta et al., 2021, Holub et al., 21 Jan 2026).
• Agent–Agent and Human–Machine Dialogues: In advanced educational or QA systems, distinct “role-specialized” agents (e.g., Student–Teacher and Teacher–Educator in Socratic frameworks) generate and critique questions, supporting iterative refinement (Holub et al., 21 Jan 2026).

2.3 Adaptive Feedback and Memory Integration

• Dynamic Memory Structures: Tree-structured, session-based, or context-wise memory units accumulate user responses, extracted features, and topic summaries, supporting both adaptive questioning (e.g., shallow-vs-deep followup) and cross-topic consistency in scoring (Hu et al., 15 Aug 2025).
• Adequacy/Uncertainty Evaluation: Dedicated evaluators (e.g., adequacy agents in mental health assessments or ActiveDropout in NLU) determine whether existing responses resolve informational needs, triggering further inquiry as needed (Hu et al., 15 Aug 2025, Datta et al., 2021).

3. Domains and Application Scenarios

3.1 Education and Curiosity Development

Interactive questioning in the classroom context leverages real-time formative assessment tools (e.g., InkSurvey) and simulation-driven open-format prompts to elicit metacognitive engagement and a broad spectrum of student-generated questions. Categorical analysis of questions and immediate instructional adjustments based on live submission streams promote both state curiosity and durable question-generation skills (Kowalski et al., 2013).

3.2 Dialogue Agents and Question-Driven Learning

Reinforcement learning frameworks that integrate explicit ASK actions (clarifications, hints) enable dialogue agents to achieve superior task success, particularly under incomplete knowledge or input ambiguity. The trade-off between the frequency of asking and answer accuracy is regulated via application-sensitive cost terms and can be directly optimized via value-based or actor-critic methods (Li et al., 2016, Misra et al., 2017).

Question-driven learning in LLMs leverages interactive, student–teacher dialogues (INTERACT), showing substantial increases (up to 25 pp) in quiz accuracy over static lesson baselines and demonstrating that active, iterative inquiry is superior to passive exposure for concept acquisition (Kendapadi et al., 2024).

3.3 Active Learning and Selective Querying

In active learning, interactive questioning is recast as selective oracle querying: models (e.g., SoQal) dynamically determine, for each informative instance, whether to request an oracle label or to self-label, guided by confidence, learned error prediction, and statistical separability thresholds (e.g., Hellinger distance on selector outputs). This dual-decision interactivity reduces annotation costs and mitigates propagating oracle errors (Kiyasseh et al., 2020).

3.4 Knowledge Base Reasoning

Semantic QA pipelines augmented by interaction (IQA, Interactive-KBQA) solicit user clarification—often binary “yes/no” or option ranking—on ambiguous semantic parses or candidate graph patterns. Option Gain or information-gain criteria systematically maximize unresolved prior uncertainty per user click. This interactive loop vastly reduces the search/pruning cost versus static ranking and yields substantially higher F₁ and usability ratings (Zafar et al., 2020, Xiong et al., 2024).

3.5 Clinical and Assessment Dialogue

Clinical assessment frameworks (e.g., AgentMental) utilize multi-agent collaboration—questioning, adequacy evaluation, scoring, and updating—within an iterative questioning loop. Adequacy scoring (necessity thresholds) triggers tailored follow-ups per topic; memory trees organize evidence and promote non-redundancy, supporting explainable, accurate scoring on complex instruments (e.g., PHQ-8) (Hu et al., 15 Aug 2025).

3.6 Human–Robot and Multimodal Disambiguation

Interactive questioning in multimodal settings (e.g., MIEL for exophora resolution in robotics) leverages LLMs (e.g., GPT-4o) to resolve persistent ambiguity after initial multimodal fusion. The system invokes clarifying questions only when needed (one-shot), folding new user responses back into the prediction pipeline, resulting in substantial gains in ambiguous scenarios (Oyama et al., 22 Aug 2025).

4. Measurement, Assessment, and Impact

Quantitative assessment of interactive questioning processes employs both direct outcome metrics and meta-analyses of interaction structure:

• Class/Answer Accuracy and Learning Gains: Outcome-centric, measuring improvement in accuracy after iterative questioning (e.g., 25% relative gain in INTERACT after 5 turns) (Kendapadi et al., 2024).
• Question Distribution Analysis: Taxonomy-based tallying of question types (e.g., proportion congruous/incongruous/creative) to quantify curiosity or inquiry diversity (Kowalski et al., 2013).
• Operational Efficiency: Oracle ask-rate, interaction cost (e.g., mean number of required interactions), and reduction in annotation load (Kiyasseh et al., 2020).
• User-Centric Evaluations: Likert-scale surveys (e.g., clarity, relevance, depth) and cognitive or clinical endpoints (e.g., F1 score, Kappa) (Holub et al., 21 Jan 2026, Hu et al., 15 Aug 2025).
• Error Analysis and Intervention: Annotation of error types (entity linking, predicate search, reasoning, hallucination) guides targeted human intervention and system refinement (Xiong et al., 2024).

5. Design Principles and Best Practices

Empirical and theoretical findings across domains converge on several core design principles:

• Decoupling Acquisition and Querying: In both AL and dialogue, first identify high-information points, then decide whether to consult an oracle/human or proceed autonomously (Kiyasseh et al., 2020).
• Explicit Cost–Benefit Trade-offs: Regulatable parameters for the cost of asking (e.g., annoyance, annotation, delay) ensure judicious use of interaction (Li et al., 2016, Yu et al., 2019, Kowalski et al., 2013).
• Curiosity and Inquiry Taxonomies: Openly sharing question-type frameworks with users/agents scaffolds meta-inquiry and self-diagnosis, promoting diverse, balanced questioning (Kowalski et al., 2013, Datta et al., 2021).
• Human-in-the-Loop for Uncertainty: Dynamic routing to human experts when confidence/adequacy is low prevents cascading errors and ensures robustness (Datta et al., 2021, Hu et al., 15 Aug 2025).
• Memory and Context Integration: persistently track dialog/per-topic memory to avoid redundant queries and longitudinally accumulate evidence (e.g., tree memories in assessment) (Hu et al., 15 Aug 2025).
• Interactive Loop Termination Criteria: Dynamic/adequacy-based stopping (e.g., Socratic “Great question!” or necessity scores), rather than fixed-length dialogue, yield both higher relevance and engagement (Holub et al., 21 Jan 2026).

6. Challenges and Open Directions

Despite advances, several persistent challenges remain:

• Scalability of Real-time Annotation: Managing the cognitive and computational load as dialog complexity and system scale increase, especially in open or multimodal domains (Xiong et al., 2024, Hu et al., 15 Aug 2025).
• Generalization Beyond Static Domains: Adapting interactive questioning to unfamiliar ontological or conceptual spaces without human-crafted exemplars or rules (Kendapadi et al., 2024).
• Hybridization with Generative/LLM Models: Achieving robust, interpretable behavior as systems increasingly incorporate powerful, yet opaque, generative LLMs (Holub et al., 21 Jan 2026, Oyama et al., 22 Aug 2025).
• Benchmarking and Standardization: Development of comprehensive, multi-turn datasets and evaluation protocols for benchmarking interactive questioning at scale across domains (Biancofiore et al., 2022).
• Explainability and User Trust: Mechanisms for users to audit, interpret, and control the questioning process and its outcomes, particularly in high-stakes or clinical settings (Hu et al., 15 Aug 2025, Zafar et al., 2020).

In summary, interactive questioning processes comprise a broad methodological spectrum, unifying information-seeking, adaptive dialog, and feedback-driven learning. By dynamically selecting, generating, and responding to questions based on state, uncertainty, and user input, these processes enable agents—human and artificial—to efficiently acquire knowledge, resolve ambiguity, and guide collaborative exploration across educational, computational, and decision-making domains.