Research and Development

Interactive Storytelling and Adaptive Instructional Systems for Character Education


Interactive stories can be utilized as classroom and homework exercises for character education courses. Adaptive instructional systems can select and sequence these interactive stories to provide individualized assessment and instruction for learners. While presenting learners with interactive stories, at scale, adaptive instructional systems can simultaneously measure and evaluate the interactive stories. These evaluations and related data about learners’ bulk interactions can be of use for continually producing new and better interactive stories.

Interactive Storytelling

In (Arthur, Kristjánsson, Harrison, Sanderse, & Wright, 2016), the authors state that “rather than remaining satisfied with eliciting self-evaluations of virtue, an Aristotelian approach would ideally explore how people do in fact react – attitudinally, emotionally, behaviorally – to morally-charged situations. Could this perhaps be done by exposing them to scenarios involving moral dilemmas and recording their responses?” The authors also note that it seems plausible “to conceive of dilemma tests that would attempt to home in on the virtues.”

Types of interactive stories include: guided play, role-playing games, the case method, decision games, simulations, literature and literary discussions, story-based assessment items, digital gamebooks, interactive films, and serious games. With these latter four types, story-based assessment items, digital gamebooks, interactive films, and serious games, it is straightforward to record and analyze learners’ responses and these latter four types are focused on herein.

In addition to presenting learners with a-priori choices such as “what should character X do next?”, learners could be presented with after-the-fact questions resembling “did character X do the right thing?” and, potentially, with follow-up questions resembling “why or why not?”. Interactive stories could unfold as a result of all of these varieties of choices and questions.

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

Adaptive Instructional Systems

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).

Interactive stories can be of use for both assessment and instruction and adaptive instructional systems can optimize the instructional timing of each interactive story’s instructional strategies for each individual learner.

Adaptive instructional systems can select interactive stories from arbitrarily large storybanks and sequence these stories for individual learners to play as classroom and homework exercises. Adaptive instructional systems can do so in accordance with individual learners’ pedagogical enrichment goals and while mindful of individual learners’ affective states, motivation, engagement, and flow.

Learner Modeling

Learner modeling is the means by which adaptive instructional systems can best select and sequence interactive stories for individualized learning. As learners make decisions and provide responses to the choices and questions in presented interactive stories, adaptive instructional systems can update their models of learners.

In (Paradeda, Ferreira, Martinho, & Paiva, 2017), the authors describe the use of an interactive storytelling scenario to identify players’ personality traits according to the Myers-Briggs Type Indicator theory. In (de Lima, Feijó, & Furtado, 2018), the authors describe a system which models players’ traits according to the Big Five model. By extracting the decisions made and the responses provided by players in interactive storytelling scenarios, these authors were able to predict players’ personality traits.

Beyond modeling and predicting personality traits, systems can model and predict character traits and virtues – intrapersonal values, interpersonal values, and civic virtues – via the decisions made and the responses provided as learners play interactive stories.

Open learner modeling, presenting learners with systems’ assessments of their performance, often has a positive effect on learners’ progress. Open learner modeling can promote reflection with respect to learners’ knowledge and skills, can encourage self-assessment, can support planning and monitoring, and can allow learners to take greater control and responsibility over their learning.


Psychometric tools for the assessment of moral development, moral judgment, and moral reasoning include: the moral judgment interview (MJI; Colby & 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 psychometric tools (e.g. MJI, DIT, ICM) utilize forms of interactive stories where complex situations or moral dilemmas are described and questions are subsequently presented about what story characters should do next.

Adaptive psychometric testing techniques could vary the sequencing of items selected from arbitrarily large storybanks based on learners’ decisions and responses. These techniques could also vary the contents of the items themselves, in particular when interactive story items provide instructional narrative material after learners’ decisions and responses.

Learner models and their dynamics across character education courses could be of use to educators for performing assessment, for integrating learners’ performance and progress with respect to exercises and activities into character education course grades.

Other potential components for the grading of character education courses include: the study of the history, theory, and philosophy of character and virtue, classroom discussions and participation, essays, individual and group projects, effort, and overall progress.


In addition to being instruments for assessment, interactive stories have instructional value. Beyond the instructional value of ordinary narrative, learners can learn from the consequences presented after they make decisions.

Adaptive instructional systems should sequence interactive stories optimally for individual learners with respect to the instructional strategies of the individual stories. These sequences should deliver, as possible, synergistic results.

Educational Contexts

Educational exercises will seldom be administered in isolation from other components of character educational strategies. Overarching character educational strategies and policies will often contribute to the complex contexts in which educational exercises are administered and evaluated.

The data from educational exercises, considered at scale, can be of use for comparing and contrasting different overarching character educational strategies, policies, and implementations.


With new and emerging tools, technologies, and techniques for the assessment and instruction of character and virtue, character education programs can be more precisely evaluated.

While presenting learners with interactive stories, at scale, adaptive instructional systems can simultaneously measure and evaluate the stories. These evaluations and related data about learners’ bulk interactions can be of use for continually producing new and better interactive stories.

Authoring Interactive Stories

In (Stefnisson & Thue, 2018), the authors indicate that manually creating interactive stories is inherently difficult and that there is a need for advanced authoring tools. The difficulty is further pronounced when the matter is, beyond creative writing, data-driven and efficacious interactive story design and engineering.

Artificial intelligence tools can be of use for both developing and evaluating interactive stories, screenplays, storyboards, and production schedules.

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 & Young, 2010).

In (Riedl & Young, 2006), the authors describe techniques for generating, beyond linear stories, branching or interactive stories such as those found in digital gamebooks, interactive films, and some serious games.

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, 2012).

Utilizing reader models while generating stories is discussed in (Mawhorter, 2013) where the author states that, while non-interactive story generation systems have explored reader modeling for discourse generation or presentational purposes, several interactive drama systems, such as IDtension and U-Director, have utilized formal models of their users to evaluate narrative possibilities. The author notes that IDtension, in particular, has a formal model which “addresses the users’ perceptions of ethical consistency, motivation, relevance, complexity, progress, and conflict.”

In (Barber & Kudenko, 2007), the authors 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 a 2009 publication, the authors present the GADIN system (Barber & Kudenko, 2009).

Contemporary approaches to generating stories also include neural story generation (Alabdulkarim, Li, & Peng, 2021) as well as hybrid, or neurosymbolic, techniques.

Related Work

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 courses. Adaptive instructional systems can select and sequence these interactive stories to provide individualized assessment and instruction for learners. While presenting learners with interactive stories, at scale, adaptive instructional systems can simultaneously measure and evaluate the interactive stories. These evaluations and related data about learners’ bulk interactions can be of use for continually producing new and better interactive stories.


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