Research and Development

Advancing Character Education with Interactive Storytelling and Adaptive Instructional Systems


In 2009, a report by the U.S. Department of Education’s Institute of Education Sciences was released (Person, Moiduddin, Hague-Angus, & Malone, 2009). Despite previous studies indicating the benefits of character education programs, and after decades of visibility as a national educational priority, only 13 programs made the evidentiary cut established by the report. Furthermore, only 5 of those 13 were at least potentially efficacious in influencing knowledge, attitudes, and values, only 3 in influencing behavior, and just 1 program in influencing academic achievement (Sojourner, 2012).

This article intends to advance character education and social and emotional learning by describing a rigorous pedagogical approach called for by that report.

New educational programs are envisioned which utilize interactive stories, e.g., interactive films and serious games, as exercises. These exercises can instruct learners while assessing them.

Adaptive instructional systems can administer these exercises to learners. To provide personalized and optimized instruction and assessment, these systems model learners. Modeling learners is particularly useful for the domains of character education and social and emotional learning. This article discusses the adaptive game-based psychometric assessment of intrapersonal values, interpersonal values, and civic virtues.

By introducing exercises to character education and social and emotional learning programs, these educational programs can be more precisely evaluated and continuously improved.

Interactive Storytelling

Types of interactive stories include: guided play, role-playing games, the case method, decision games, simulations, literature and literary discussions, story-based items, digital gamebooks, interactive films, and serious games.

These latter four types of interactive stories – story-based items, digital gamebooks, interactive films, and serious games – can be administered by computer and, accordingly, it is straightforward to record and to analyze learners’ decisions and responses. For these reasons, these latter four types are focused on herein.

Opportunities for learners to interact with interactive stories can be provided before, during, or after story content, with learners’ responses potentially shaping the stories as they unfold. The instructional value of interactive stories greatly surpasses that of ordinary stories as, after being presented with choices, learners can be presented with consequences of their decisions.

In addition to presenting learners with choices resembling “what ought character X do next?”, interactive stories could present after-the-fact questions resembling “did character X do the right thing?” and present follow-up questions resembling “why or why not?”.

Not every choice or question presented to learners need have one simply correct answer. Some choices or questions may have more than one correct answer and others may have none.

Adaptive Instructional Systems

Adaptive instructional systems are educational technologies which select and sequence exercises to provide learners with individualized and optimized instruction and assessment. Types of adaptive instructional systems include: intelligent tutoring systems, educational recommender systems, and intelligent media.

Horace Mann, a pioneer of public schooling and modern education, felt that “one of the most important concepts for teachers to understand and implement pertaining to character education is the correct use of instructional timing, as well as the proper implementation strategy, when considering moral development in students” (Watz, 2011). Adaptive instructional systems can optimize the instructional timing of interactive story exercises’ instructional strategies.

Interactive story exercises should be administered to their intended audiences so as to be both appropriate to age and stage of development. Across stages of human development, changes occur in terms of moral schemas (Narvaez, 2002), social cognition, theory of mind, and imagination. With respect to the development of imagination, Gajdamaschko (2006) indicates that, according to Vygotsky, the imagination undergoes developmental shifts which profoundly impact learners’ cultural, intellectual, personality, behavioral, and sensemaking capabilities.

Adaptive instructional systems could have amongst their selection criteria that selected exercises be thematically relevant to the topics under discussion in character education courses. To enhance the thematic variety of exercises, however, systems could intersperse exercises from both previous and forthcoming course topics.

Adaptive instructional systems could intersperse interactive story exercises intended for use across multiple character education and social and emotional learning programs, e.g., standardized assessment items.

Adaptive instructional systems could make use of groups of exercises, referred to also as “testlets” or “panels”. These are discussed in Wainer, Dorans, Eignor, Flaugher, Green, Mislevy, Steinberg, and Thissen (2000) and, taken as units of activity construction and analysis, can mitigate context effects, item ordering, and content balancing difficulties.

Modeling Character

Peterson and Seligman (2004) provide a universal catalog of character strengths and virtues. This catalog is inspired from a collection of many dozens of historical and contemporary inventories and it can be of use when designing character education and social and emotional learning programs.

Harvard’s EASEL Laboratory’s Taxonomy Project indicates that the field of social and emotional learning is structured around “a large number of organizational systems or frameworks that often use different or even conflicting terminology to talk about a similar set of skills.” The Taxonomy Project seeks to “create greater precision and transparency in the field of social and emotional learning and to facilitate more effective translation between research and practice”, providing “information and tools that summarize and connect the major frameworks.”

Interactive story exercises and classroom discussions can also support moral literacy. “If we want our children to possess the traits of character which we most admire, we need to teach them what those traits are. They must learn to identify the forms and content of those traits” (Bennett, 1988). The abilities of recognizing character traits in oneself and others in situational contexts – and the related vocabulary skills – are essential components of cultural and moral literacy. Interactive story exercises can instruct and assess with respect to moral literacy and related vocabulary skills by asking learners about the particular traits exhibited by characters in depicted scenarios.

Modeling Learners

Modeling learners is how adaptive instructional systems can best select and sequence interactive stories for individualized and optimized instruction and assessment. Adaptive instructional systems can use the decisions made and responses provided by learners as they play to model their personality traits, intrapersonal values, interpersonal values, and civic virtues.

Existing psychometric instruments for measuring moral development, moral judgment, and moral reasoning include: the moral judgment interview (MJI; Colby, Abrahami, & Kohlberg, 1987), the sociomoral reflection measure (SRM; Gibbs & Widaman, 1982), the defining issues test (DIT, DIT-2; Rest, 1979), and the intermediate concepts measures (ICM; Bebeau & Thoma, 1999). Most of these instruments utilize story-based items, describing complex situations or moral dilemmas and subsequently presenting questions about what story characters ought do next.

Paradeda, Ferreira, Martinho, and Paiva (2017) describe the use of an interactive storytelling scenario to identify personality traits according to the Myers-Briggs Type Indicator theory. De Lima, Feijó, and Furtado (2018) describe a system which models traits according to the Big Five model. By extracting the decisions made and responses provided during the play of interactive stories, personality traits can be modeled and predicted.

Cutler and Montgomery (2014) describe adaptive psychometric inventories, techniques for including large batteries on surveys while minimizing the number of questions that respondents must answer.

According to Mislevy, Oranje, Bauer, von Davier, Hao, Corrigan, Hoffman, DiCerbo, and John (2014), a challenge today is to extend the accomplishments of psychometrics methodologies, e.g., statistical inference and probability-based reasoning, “from applications to relatively sparse and encapsulated data, for inferences cast in trait and behaviorist psychology, to the richer data made possible in interconnected digital environments, for inferences cast in contemporary sociocognitive psychologies, as encountered for example in game-based assessment.”

Interactive stories can be utilized for modeling and predicting personality traits, intrapersonal values, interpersonal values, and civic virtues. Techniques from computerized adaptive psychometric testing can be of use for providing learners with personalized and optimized sequences of these stories as exercises.

Open learner modeling is encouraged for character education and social and emotional learning programs. Open learner modeling provides learners with access to systems’ models and assessments of their performance and this often has a positive effect on their progress. Open learner modeling can promote reflection, encourage self-assessment, support planning and monitoring, and allow learners to take greater control and responsibility over their learning.

Modeling Exercises and Progressions

Partlan, Carstensdottir, Snodgrass, Kleinman, Smith, Harteveld, and El-Nasr (2018) detail 24 distinct metrics for interactive stories with these metrics organized into three categories: narrative structural complexity, action space, and interactive affordances.

Carstendottir (2020) discusses the modeling of interactive stories and learners’ progressions through them. She describes that the history of interactive storytelling is comprised of two broad approaches, system-centric and player-centric. This article builds on player-centric (learner-centric) approaches to educational interactive storytelling. Beyond branching as a result of learners’ responses or decisions, interactive stories may branch as a result of: models of learners, learners’ mental states (e.g., affect, mood, attention, motivation, engagement, or flow), learners’ response times, settings and configurations, data, variables, program logic, or random numbers.

She indicates that both system-centric and player-centric historical approaches to interactive storytelling have made use of graph-based formalisms. One benefit of graph-based formalisms is that, as learners play interactive-story-based exercises, they traverse paths across their graph-based representations. These paths could be utilized for purposes of mathematical modeling and analysis, e.g., measuring the internal consistency, or statistical interrelatedness, between paths from traversals of different exercises in sets of exercises.

Event-stream processing is another approach to game analytics. In this approach, as learners play games, they produce streams of typed events which can be processed in real-time and/or logged for subsequent analysis.

Mislevy, Corrigan, Oranje, DiCerbo, Bauer, von Davier, and John (2016) detail approaches to game analytics and psychometrics based on evidence and argumentation, providing a number of measurement models with which to synthesize “nuggets of evidence” from across observations. They state that using a measurement model is one way to “accumulate information across multiple sources of evidence, expressed as belief about characteristics of players whether transitory or persistent,” and that so doing “provides tools to sort out evidence in complicated circumstances, quantify its properties, and flexibly assemble evidence-gathering and evidence-accumulating components.”

It will be important to create and utilize a shared, common vocabulary for data logs so that educational data can be readily accumulated and processed from interactive story exercises produced by multiple vendors.

Modeling Choices

Mawhorter, Mateas, Wardrip-Fruin, and Jhala (2014) describe choice models as consisting of the framings of, options presented for, and anticipated and subsequent outcomes associated with choices. With respect to their theory of choice poetics, they outline three main avenues of investigation: mode of engagement, choice idioms, and dimensions of player experience.

Aspects of mode of engagement include player perspective, motivation, and the particulars of play practice. There could be individual differences with respect to mode of engagement, different types of play and different types of players. A partial list of types of play includes: avatar play, role play, power play, exploratory play, analytical play, and critical play.

Choice idioms are generic structures or patterns for entireties or parts of choices which generally achieve specific effects. A partial list of types of choices includes: dead-end option, false choice, blind choice, dilemma, flavor choice, delayed effect, puzzle choice, and unchoice.

A partial list of dimensions of player experience includes: agency, influence, autonomy, identification, transportation, absorption, responsibility, and regret.

Modeling Situations and Contexts

Situation models are representations, e.g., cognitive or computational representations, of states of affairs such as those relayed through stories. Situation models are relevant for modeling the framings of choices presented to learners in interactive stories. Learners’ situation models can be described as being their mental representations of the states of affairs in stories, e.g., as they encounter choices or questions.

In addition to being relevant to understanding reading and film comprehension, the perception and mental representations of situations and contexts are matters of considerable importance to the behavioral sciences. Cantor (1981), for instance, presents that there may be conceptual prototypes for situations, categories and taxonomies of situations, and that situations may have traits, features, or attributes. She indicates that understanding the perception and mental representations of situations is useful for understanding the processes of generalizing over observations of behaviors as occurring in situational contexts.

Wyer (2003) indicates that situation models are relevant to both social cognition and moral judgment.

Modeling Decision-making Processes

Sanfey and Chang (2008) indicate that “within judgment and decision making, many multiple-processing theories have been proposed, all of which posit different fundamental modes of processing that alternately cooperate and compete in reaching a decision.” They note that while “there are nuances specific to each theoretical conception, for the most part, these dual-process models are all structurally very similar.” These models each include both automatic and volitional processing operating in parallel. Van den Bos (2018) indicates that intuitive and deliberative processes may operate in parallel with respect to moral judgment.

For these reasons, educational data could reasonably include response times, mental chronometry data, from learners’ interactions with the choices presented by interactive story exercises.

Adaptive instructional systems could hypothesize about how competing values or principles were ranked or weighed by learners when making decisions.

Modeling Learning

The modeling, measurement, analysis, and visualization of learners’ course progressions as pertaining to multiple, simultaneous, interrelated course objectives are topics of acute interest.

Models of learners and their course progressions formed by adaptive instructional systems could be of use to educators with respect to course grading. Other potential components for course grading include: the study of the history, theory, and philosophy of character and virtue, classroom discussions and participation, individual and group projects, essays, and overall effort and progress.

Arthur, Kristjánsson, Harrison, Sanderse, and Wright (2016) discuss how educators should measure virtue and evaluate character education programs. They indicate that program evaluators should desire evidence of improvement in virtue literacy, evidence of improvement in moral behavior, evidence of their interrelation, and evidence that educational programs were causal to these improvements.

Interactive story exercises and adaptive game-based psychometric assessment could be important components of a patchwork of methods with which to triangulate upon and measure these factors.

Some exercises could serve as standardized assessment items and be interspersed across different programs by adaptive instructional systems.

A/B testing could be performed between groups of learners whom have participated in character education and social and emotional learning programs and groups whom have not.

While exercises could help to strengthen moral knowledge, moral reasoning, moral sensitivity, moral judgment, and moral literacy, learners must determine to habitually apply these to their real-world behavior.

Modeling Educational Contexts and Climates

Interactive story exercises will seldom be administered in isolation from educational settings. Overarching educational policies, plans, strategies, and school cultures will often contribute to the complex educational contexts and climates in which learners encounter exercises.

Multilevel modeling can be of use for simultaneously modeling learners, groups, classes, schools, districts, states, and countries. Ma, Ma, and Bradley (2008) discuss how multilevel modeling can be of use for investigating school effects.

They use the term “context variables” to refer to the “hardware” of schools, variables such as physical backgrounds (e.g., school location and resources), student bodies (e.g., school socioeconomic and racial-ethnic compositions), and teacher bodies (e.g., teacher education and experience).

They use the term “climate variables”, also often referred to as “evaluative variables”, to refer to the “software” of schools, with characteristics descriptive of learning environments including administrative policies, instructional organization, school operation, and the attitudes, values, and expectations of learners, parents, teachers, and administrators.

They emphasize the importance of understanding the distinction between educational contexts and climates, noting that school-effects research tends to focus on educational climate variables as these are under the direct control of learners, parents, teachers, and administrators.

Computer-aided and Automatic Item Generation

Artificial intelligence technologies can be of use for creating, producing, and evaluating interactive stories, screenplays, storyboards, and production schedules.

Stefnisson and Thue (2018) argue that manually creating interactive stories is inherently difficult and that there is a need for advanced authoring tools. This difficulty is expected to be further pronounced when the matter is, beyond one of creative writing, one of evidence-based and efficacious educational interactive story design and engineering.

Related Work

The first automatic story generation system was Automatic Novel Writer (Klein, Aeschlimann, Balsiger, Converse, Court, Foster, Lao, Oakley, & Smith, 1973) followed by TALE-SPIN (Meehan, 1977), Author (Dehn, 1981), Universe (Lebowitz, 1983), Minstrel (Turner, 1993), Mexica (Pérez y Pérez, 1999), Brutus (Bringsjord & Ferrucci, 1999), and Fabulist (Riedl, 2004).

Interactive drama systems include: Oz (Bates, 1992), DEFACTO (Sgouros, 1997), the Virtual Theater Project (Hayes-Roth, van Gent, & Huber, 1997), I-Storytelling (Cavazza, Charles, & Mead, 2002), Façade (Mateas & Stern, 2003), IDtension (Szilas, 2003), Mimesis (Young, Riedl, Branly, Jhala, Martin, & Saretto, 2004), NOLIST (Bangsø, Jensen, Jensen, Andersen, & Kocka, 2004), OPIATE (Fairclough, 2004), the Interactive Drama Architecture (IDA; Magerko, 2005), FAtiMA (Aylett, Dias, & Paiva, 2006), IN-TALE (Riedl & Stern, 2006), U-Director (Mott & Lester, 2006), SASCE (Nelson, Roberts, Isbell, & Mateas, 2006), Bards (Pizzi, Charles, Lugrin, & Cavazza, 2007), PaSSAGE (Thue, Bulitko, Spetch, & Wasylishen, 2007), DED (Arinbjarnar & Kudenko, 2008), GADIN (Barber & Kudenko, 2009), and Erasmatron (Crawford, 2013).

Riedl and Young (2006) describe techniques for automatically generating, beyond linear stories, branching interactive stories.

Barber and Kudenko (2007) describe a system which adaptively models users to generate interesting dilemma-based stories, noting that such stories require “fundamentally difficult decisions within the course of the story.” In 2009, they presented the GADIN system (Barber & Kudenko, 2009).

Artificial intelligence systems for ethics education include: PETE (Goldin, Ashley, & Pinkus, 2001), AIENS (Hodhod, Kudenko, & Cairns, 2009), Conundrum (McKenzie & McCalla, 2009), and Umka (Sharipova, 2015).


Interactive stories can be utilized as classroom and homework exercises for character education and social and emotional learning programs. Adaptive instructional systems can select and sequence these exercises for learners at scale, providing learners individualized and optimized instruction and assessment.

Discussed herein were interactive storytelling, adaptive instructional systems, and the modeling of character, learners, exercises and progressions, choices, situations and contexts, decision-making processes, learning, and the educational contexts and climates in which these occur.


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