What if artificial-intelligence systems could ask, manage, and answer questions while incrementally processing and analyzing texts?

Use cases for such capabilities would include enhancing: story comprehension and prediction, automatic item generation, synthetic data generation, automatic text summarization, and the computer-aided design and engineering of instructional materials, technical documentation, and narratives.

Historical approaches to computationally modeling the various interdependent and integrated components of reading have included modeling: eye movement, word identification, sentence processing, discourse representation, and overall reading architectures.

Computational models of reading architectures include: the Attention Shift Model, E-Z Reader, EMMA, SWIFT, Glenmore, SERIF, OB1-Reader, and Über-Reader (Reichle, 2021).

In 2006, the task-based relevance and content extraction model was developed (TRACE; Rouet, 2006). In 2011, a multi-document version was developed (MD-TRACE; Rouet & Britt, 2011). In 2017, a model was developed which expanded upon these, phrasing reading as problem-solving (RESOLV; Rouet, Britt, & Durik, 2017).

The RESOLV model argues that the activities of reading are more contextual than previous research indicated (Britt, Rouet, & Durik 2017). RESOLV proposes two cognitive representations: the context model and the task model.

There are interesting relationships between dynamic and unfolding informational needs and contexts during reading. Answering questions from requesters or from readers themselves can be components of reading tasks. Readers' goals and subgoals could include acquiring that information with which to satisfy their dynamic and unfolding informational needs.

Of mathematics, Georg Cantor said that the art of asking questions is more valuable than solving problems.

In artificial intelligence, the capability to generate the right questions is highly sought after to reflect the ability to understand language, to gather new information, and to engage with users (Ko, Chen, Huang, Durrett, & Li, 2020).

With respect to reading comprehension, it is plausible to assume that, as content is processed, previous questions are answered and new ones are raised (Olson, Duffy, & Mack, 2017).

While it is not posited that those reading silently are consciously asking themselves questions as texts are processed, texts' contents are understood and added to growing mental representations and existing informational needs, or questions, interact with arriving content to produce subsequent informational needs, or questions (Olson, Duffy, & Mack, 2017).

Along these lines, erotetic models of attention phrase attention as involving relationships between questions and their answers (Koralus, 2014).

Several models have been developed which describe cognitive processes involved in answering questions, whether from memory or from consulting external resources.

In 1978, Lehnert developed a computational model of question-answering, QUALM (Lehnert, 1978). It was implemented as a computer program that interoperated with two different story-comprehension systems.

In 1990, the QUEST model was developed (Graesser & Franklin, 1990). It was comprised of four procedural components: question interpretation, identification of relevant information sources, pragmatics, and convergence mechanisms.

Taxonomies of questions have encompassed: recognition, recall, comprehension, application, analysis, synthesis, and evaluation (Bloom, 1956).

Taxonomies of questions have encompassed: causal antecedent, goal orientation, enablement, causal consequent, verification, disjunctive, instrumental/procedural, concept completion, expectational, judgmental, quantification, feature specification, and request (Lehnert, 1978).

Later taxonomies have added: example, definition, comparison, and interpretation (Graesser & Person, 1994).

There are also to consider: guiding questions and inquisitive questions. Guiding questions are those questions provided by texts' authors to direct readers in their searches for understanding. Inquisitive questions arise from readers' curiosity.

Questions can also be described as being either: literal or inferential. Literal questions focus on information directly stated in the text, involving surface-level understandings. Inferential questions require readers to make inferences, involving deeper understandings and interpretations of the text.

Inferences during reading can be: automatic or strategic; online or offline; text-connecting, knowledge-based, or extratextual; local or global; coherence or elaborative; unconscious or conscious; bridging; intersentence or text-connecting, or gap-filling; coherence, elaborative, knowledge-based, or evaluative; and anaphoric, text-to-text, or background-to-text (Kispal, 2008).

Types of inferences during reading include: referential; filling in deleted information; inferring the meanings of words; inferring connotations of words or sentences; relating text to prior knowledge; inferences about the author; inferences about characters; inferences about the state of the world as depicted; confirming or disconfirming previous inferences; and drawing conclusions (Pressley & Afflerbach, 1995).

Types of inferences during story-reading include: referential; case structure role assignment; antecedent causal; superordinate goal; thematic; character emotion; causal consequence; instantiation noun category; instrument; subordinate goal action; state; reader's emotion; and author's intent (Graesser, Singer, & Trabasso, 1994).

Erotetic inferences are processes through which questions are inferred from assertions, questions, or combinations of assertions and questions.

Question decomposition, transforming compound questions into one or more simpler ones, is a type of erotetic inference.

Artificial-intelligence systems can be designed to update their goals and subgoals to acquire the answers to their dynamic and unfolding questions during the incremental processing and analysis of one or multiple texts.

Because answers to questions can be revised as subsequent content is processed, records or traces of asked, pending, and answered questions should be stored by artificial-intelligence systems.

Because inferences can be later confirmed or disconfirmed as subsequent content is processed, records or traces of inferencing processes should be stored by artificial-intelligence systems.

With "cognitive provenance" capabilities, correct processes of asking and answering questions and correct processes of inferencing could be rewarded and incorrect processes penalized. Pertinent machine-learning approaches include outcome-supervised and process-supervised reward models (Uesato, Kushman, Kumar, Song, Siegel, Wang, Creswell, Irving, & Higgins, 2022; Lightman, Kosaraju, Burda, Edwards, Baker, Lee, Leike, Schulman, Sutskever, & Cobbe, 2023).

Artificial-intelligence systems capable of learning how to read could be trained using curriculum learning techniques, starting with texts for younger readers and progressing through texts of increasing difficulty.

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Graesser, Arthur C., and Natalie K. Person. "Question asking during tutoring." American Educational Research Journal 31, no. 1 (1994): 104-137.

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Ko, Wei-Jen, Te-yuan Chen, Yiyan Huang, Greg Durrett, and Junyi Jessy Li. "Inquisitive question generation for high level text comprehension." arXiv preprint arXiv:2010.01657 (2020).

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Lightman, Hunter, Vineet Kosaraju, Yuri Burda, Harrison Edwards, Bowen Baker, Teddy Lee, Jan Leike, John Schulman, Ilya Sutskever, and Karl Cobbe. "Let's verify step by step." In The Twelfth International Conference on Learning Representations. 2023.

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Reichle, Erik D. Computational Models of Reading: A Handbook. Oxford University Press, 2021.

Rouet, Jean-François. The Skills of Document Use: From Text Comprehension to Web-based Learning. Routledge, 2006.

Rouet, Jean-François, and M. Anne Britt. "Relevance processes in multiple document comprehension." Text Relevance and Learning from Text (2011): 19-52.

Rouet, Jean-François, M. Anne Britt, and Amanda M. Durik. "RESOLV: Readers' representation of reading contexts and tasks." Educational Psychologist 52, no. 3 (2017): 200-215.

Uesato, Jonathan, Nate Kushman, Ramana Kumar, Francis Song, Noah Siegel, Lisa Wang, Antonia Creswell, Geoffrey Irving, and Irina Higgins. "Solving math word problems with process- and outcome-based feedback." arXiv preprint arXiv:2211.14275 (2022).