Research and Development

Advancing Design and Engineering with Adaptive Instructional Systems

Adaptive Instructional Systems

Adaptive instructional systems, e.g., intelligent tutoring systems, can select and sequence design and engineering exercises for students. While so doing, these AI systems could measure and model students with respect to their proficiencies and adaptive expertise.

To achieve these objectives, adaptive instructional systems could receive and process real-time screencasts, streams of video and data generated as students complete design and engineering exercises. This real-time multimodal communication would enable adaptive instructional systems to better provide students with instruction, assessment, and contextual assistance.

This bulk educational data could be retained by adaptive instructional systems (1) to provide portions of recordings to other students, (2) to continuously retrain and improve, (3) to train other AI systems, (4) to provide resources for the advancement of education and science.

Screencast Streams and Recordings

State-of-the-art design and engineering software include features for recording and live-streaming screencasts. The formats of these streams and recordings can exceed those for simple video as screencast streams and recordings can include user-input events, e.g., keyboard and mouse events, and data representing users' commands and actions. Similarly, modern video communication technologies support screen-capturing and there are also remote desktop software to consider.

In (Grossman, Matejka, & Fitzmaurice, 2010), the authors state that "storing a document's workflow history, and providing tools for its visualization and exploration, could make any document a powerful learning tool."

In (Bao, Li, Xing, Wang, & Zhou, 2015), the authors present a computer-vision-based video-scraping technique to "automatically reverse-engineer time-series interaction data from screen-captured videos."

In (Frisson, Malacria, Bailly, & Dutoit, 2016), the authors describe a general-purpose tool for observing application usage and analyzing users' behaviors, combining computer-vision-based analyses of video-recordings with the collection of low-level interactions.

In (Sadeghi, Dargon, Rivest, & Pernot, 2016), the authors present a framework for fully capturing processes of computer-aided design and engineering.

Process Mining and Discovery

Process mining and discovery are relevant to the processing of screencast streams and recordings. Process mining is a family of techniques supporting the analysis of operational processes based on event logs. Process discovery aims to robustly obtain process models which describe event logs. Process models act as representations of processes (e.g., Petri nets, BPMN, activity diagrams, state diagrams).

In (Chang, Lafreniere, Kim, Fitzmaurice, & Grossman, 2020), the authors introduce workflow graphs which encode "how the approaches taken by multiple users performing a fixed 3D design task converge and diverge from one another."

Modeling Learners

Modeling learners is how adaptive instructional systems can best select and sequence design and engineering exercises for individualized learning.

Functions of adaptive instructional systems, e.g., intelligent tutoring systems, for design and engineering will include asessing students' (1) specific design and engineering proficiencies, (2) specific proficiencies pertaining to software use, and (3) adaptive expertise, as exhibited in screencast recordings of completions of exercises.

Adaptive expertise, a concept introduced by Hatano and Inagaki in 1986, has long been a useful construct though a challenging one to measure, requiring sophisticated analyses including based on self-reporting instruments such as Fisher and Peterson's Adaptive Expertise Beliefs survey.

In (Pierrakos, Anderson, & Welch, 2016), the authors state that "given the importance of adaptive expertise to the emerging engineer, it is imperative that a measure of adaptive expertise be able to provide meaningful information regarding how students perform on the various dimensions of the construct." The authors argue for "the development of a more sensitive, more direct measure of adaptive expertise that can be used across different design challenges."

Modeling Learning

Adaptive instructional systems for design and engineering will also need to model students' progressions, improvements, and learning.

In (Ozturk, Yalvac, Peng, Valverde, McGary, & Johnson, 2013), the authors indicate that, "as expected, when the students were more experienced with the modeling practice, their metacognitive adaptive expertise characteristics were enhanced."

Modeling Exercises

Modeling design and engineering exercises will be useful for adaptive instructional systems and for other consumers of resultant educational data.

Exercises include natural-language problem descriptions. These problem descriptions are a type of design specification or requirements.

Exercises can be presented to students in pedagogical contexts. Specific techniques may be desired to be shown by students, e.g., certain proficiencies in the use of CAD and CAE software.

Exercises can have metadata including those useful for categorizing screencast recordings of students' design and engineering workflows for subsequent processing. Metadata for exercises might include those relating exercises to types of courses or proficiencies.

The modeling of exercises encompasses measuring their efficacy or utility as they are presented to populations of students.

Computer-aided and Automatic Item Generation

Software tools can be envisioned with which to more rapidly develop design and engineering exercises for students.

A longer-term goal is that of automatic item generation, the automatic generation of efficacious design and engineering exercises such that retained screencast recordings of students' design and engineering workflows add value to educational and scientific data resources.

Educational Data

Vast amounts of data will be required for training next-generation AI systems (1) to tutor students in design and engineering, (2) to assist designers and engineers in their tasks, and (3) to automatically perform some design and engineering tasks. As envisioned, these requisite training data will include screencast recordings of design and engineering workflows and resultant 3D models.

Possibilities for obtaining these requisite training data include (1) creating resources for users to share their open-source screencast recordings and designs with one another, e.g., resembling YouTube or GitHub, and (2) retaining those screencast recordings and designs produced by students.

Adaptive instructional systems could record and subsequently retain these data for reasons including (1) to provide portions of recordings to other students, (2) to continuously retrain and improve, (3) to train other AI systems, (4) to provide resources for the advancement of education and science.

Adaptive instructional systems, e.g., intelligent tutoring systems, can be developed which optimize for multiple objectives while selecting and sequencing design and engineering exercises. These systems could achieve pedagogical goals for students while simultaneously bootstrapping and managing large-scale, open-source educational and scientific data resources. That is, all things being equal, adaptive instructional systems can intersperse design and engineering exercises to students such that resultant data, e.g., the various design processes utilized by students when completing the exercises, would be of use when collected at scale.


Intelligent Computer Systems in Engineering Design: Principles and Applications by Staffan Sunnersjö, Intelligent Computer-aided Design Systems: Past 20 Years and Future 20 Years by Tetsuo Tomiyama, The Ethical Use of Data in Education: Promoting Responsible Policies and Practices edited by Ellen B. Mandinach and Edith S. Gummer, Chronicle: Capture, Exploration, and Playback of Document Workflow Histories by Tovi Grossman, Justin Matejka and George Fitzmaurice, Reverse Engineering Time-series Interaction Data from Screen-captured Videos by Lingfeng Bao, Jing Li, Zhenchang Xing, Xinyu Wang and Bo Zhou, InspectorWidget: A System to Analyze Users Behaviors in their Applications by Christian Frisson, Sylvain Malacria, Gilles Bailly and Thierry Dutoit, Capturing and Analysing How Designers Use CAD Software by Samira Sadeghi, Thomas Dargon, Louis Rivest and Jean-Philippe Pernot, Workflow Graphs: A Computational Model of Collective Task Strategies for 3D Design Software by Minsuk Chang, Ben Lafreniere, Juho Kim, George Fitzmaurice and Tovi Grossman, Analysis of Contextual Computer-aided Design (CAD) Exercises by Elif Ozturk, Bugrahan Yalvac, Xiaobo Peng, Lauralee M. Valverde, Prentiss D. McGary and Michael Johnson, Examining the Role of Contextual Exercises and Adaptive Expertise on CAD Model Creation Procedures by Michael D. Johnson, Elif Ozturk, Lauralee M. Valverde, Bugrahan Yalvac and Xiaobo Peng, An Examination of the Effects of Contextual Computer-aided Design Exercises on Student Modeling Performance by Michael D. Johnson, Xiaobo Peng, Bugrahan Yalvac, Elif Ozturk and Ke Liu and Measuring Adaptive Expertise in Engineering Education by Olga Pierrakos, Robin D. Anderson and Cheryl A. Welch.

Advancing Character Education with Interactive Storytelling and Adaptive Instructional Systems


Interactive stories can be utilized as classroom and homework exercises and activities for character education programs. 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. The 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 this reason, these four types are considered herein.

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

The modeling and assessment of learners and their progressions, along with the measurement and evaluation of interactive story items, can enhance educators’ understandings of what works and why, allowing educators to continually improve character education exercises, activities, and programs.

Interactive Storytelling

Interactive stories are those stories enhanced with interactivity. Opportunities for learners to interact can be presented before, during, and after story content, with learners’ responses potentially shaping the stories as they unfold. In addition to interactive stories varying as a result of learners’ responses, they can also vary as a result of: models of learners, mental states of learners (e.g., affect, mood, attention, motivation, engagement, or flow), response times, random numbers, settings and configurations, data, variables, and program logic.

Interactive stories can be utilized as classroom and homework exercises and activities for character education programs. Interactive stories can be of use for both instruction and assessment. The instructional value of interactive stories greatly surpasses that of ordinary stories as learners can be presented with choices and, subsequently, consequences of their decisions. The decisions and responses that learners provide for the choices presented to them are of use for modeling and assessing learners and their progressions.

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 note that it seems plausible “to conceive of dilemma tests that would attempt to home in on the virtues.”

Psychometric instruments for measuring 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 instruments utilize story-based items, describing complex situations or moral dilemmas and subsequently presenting questions about what story characters ought do next.

In addition to presenting learners with choices such as “what ought character X do next?”, learners could be presented with after-the-fact questions resembling “did character X do the right thing?” and 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 correct answer and others may have none.

Beyond strengthening moral knowledge, moral reasoning, moral sensitivity, and moral judgment, interactive story items and classroom discussions can 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. These abilities and skills, too, can be strengthened by character education programs.

Some interactive story items can instruct and assess with respect to moral literacy and related vocabulary skills by asking learners which particular traits were exhibited by characters in depicted scenarios.

Adaptive Instructional Systems

Adaptive instructional systems are technologies which select and sequence interactive story items to provide learners with individualized and optimized instruction and assessment. Types of adaptive instructional systems include: intelligent tutoring systems, 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 items’ instructional strategies.

Interactive story items should be administered to their intended audiences so as to be both appropriate to age and stage of development. During 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, in (Gajdamaschko, 2006), it is indicated that, according to Vygotsky, the imagination undergoes developmental shifts which profoundly impact learners’ cultural, intellectual, personality, behavioral, and sensemaking capabilities. According to Vygotsky, the development of personality, identity, and thinking also occur in imaginary worlds of heroes, boundary testing, behavior rehearsal, pretense, and play.

Adaptive instructional systems could have amongst their item selection criteria that selected items be thematically relevant to the topics under discussion in character education courses. To enhance the thematic variety of educational exercises and activities, interactive story items could be interspersed which are relevant to previously encountered as well as to as of yet unencountered course topics.

The optimal duration of interactive story items, per age and stage of development, is a matter for empirical study. Some items could require more time to complete than others. For efficiency, to avoid having to spend time introducing characters, collections of items could reuse characters or ensembles of characters.

Adaptive instructional systems can intersperse interactive story items intended for use across multiple character education programs, e.g., standardized assessment items.

Modeling Character

In (Peterson & Seligman, 2004), the authors present a universal catalog of character strengths and virtues. This catalog is inspired from a collection of many dozens of historical and contemporary inventories of strengths and virtues. This catalog can be of use when designing character education programs and learner models.

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.”

Modeling Learners

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

In (Paradeda, Ferreira, Martinho, & Paiva, 2017), the authors describe the use of an interactive storytelling scenario to identify personality traits according to the Myers-Briggs Type Indicator theory. In (de Lima, Feijó, & Furtado, 2018), the authors 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.

In (Cutler & Montgomery, 2014), the authors describe adaptive personality inventories, techniques for including large personality batteries on surveys while minimizing the number of questions that each respondent must answer.

Just as collecting the data from decisions made in interactive stories can be of use for modeling and predicting personality traits, so too can it be of use for modeling the character and values of learners. Just as techniques from computerized adaptive testing can be of use to the efficient measurement of personality, so too can they be of use for measuring character and values.

Open learner modeling is encouraged for character education programs. Open learner modeling provides learners with access to systems’ models and assessments of their performance and this often has a positive effect on learners’ 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

Modeling exercises and activities, interactive story items, can be of use to adaptive instructional systems.

There are expected to be different kinds of interactive story items – items designed to instruct and assess differently with respect to different things – to be interspersed by adaptive instructional systems.

Interactive story items may be similar, or even isomorphic, to one another. There may be a number of ways to interrelate interactive story items to one another – an ontology.

Exercise modeling topics also include item metadata, e.g., descriptions of items’ intended audiences. In these regards, existing learning object metadata formalisms can be of use.

Research into the computational modeling of narrative can be applied to the modeling of the narrative content in interactive story items. As envisioned, interactive story items will often be branching narratives. Narrative content will precede choices presented, contributing to the context and framing of the choices, and branches of narrative content will follow choice points, presented as consequences or outcomes of learners’ decisions or responses. These narrative outcomes, in turn, can precede subsequent choices.

Modeling Choices

Modeling the choices presented to learners in interactive stories can be of use to adaptive instructional systems.

In (Mawhorter, Mateas, Wardrip-Fruin & Jhala, 2014), the authors describe choice models as consisting of the framing of, options presented for, and outcomes associated with choices.

Situation models are relevant when modeling the framing of choices presented in interactive stories. In (Wyer, 2003), the author indicates that situation models are additionally relevant when modeling social cognition and moral judgment.

Modeling Decision-making Processes

Adaptive instructional systems can better model learners by also modeling their decision-making processes.

In (van den Bos, 2019), the author indicates that intuitive and deliberative processes may operate in parallel with respect to moral judgment. This suggests that multiple, parallel models of decision-making, along with response timing data, could be of use.

Adaptive instructional systems could, for instance, model how competing values or principles were ranked or weighed by a learner when making a decision.

Modeling Learning

Understanding that multiple types of interactive story items – items designed to instruct and assess differently with respect to different things – can be interspersed by adaptive instructional systems throughout character education courses, the modeling and measurement of learning, as it pertains to multiple, simultaneous, interrelated, granular educational objectives, is a topic of interest.

In addition to collecting the decisions made in interactive stories by learners, mental chronometry, the utilization of response time data, may also be a component of modeling some learning processes.

The models of learners and learning formed by adaptive instructional systems can be of use to educators when grading character education courses. 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, effort, and overall progress.


Regarding the traits and virtues versus situationism debate, a topic pertinent to both the modeling of learners and choices, how can learner modeling best generalize across a set of observations of decisions occurring in choice contexts to attribute learners with, or to provide scores for, character traits or virtues?

Moral knowledge and reasoning versus moral action is a complex topic as, while exercises and activities can strengthen moral knowledge and reasoning, learners must determine to habitually apply these with respect to their real-world behavior.

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 studies indicating 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; 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). Enhancing character education and social-emotional learning programs with computer-administered exercises and activities can provide a paradigm shift and a renewed rigor called for in the 2009 report.

Multimedia-based interactive story items, interactive films and serious games, are expected to outperform text-based items, story-based items and digital gamebooks.

Computer-aided and Automatic Item Generation

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. This difficulty is further pronounced when the matter is, beyond one of creative writing, one of evidence-based and efficacious interactive story design and engineering.

In (Carstensdottir, 2020), the author describes tools for design support and for computer-aided and automated interactive story analysis and evaluation.

Artificial intelligence technologies can be of use for both the development and evaluation of 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 and activities for character education programs.

Adaptive instructional systems are technologies which select and sequence interactive stories to provide learners with individualized and optimized instruction and assessment.

While administering interactive story items to learners at scale, adaptive instructional systems can measure and evaluate the interactive story items. These items will seldom be administered in isolation from other components of character educational strategies. Overarching character educational policies, plans, and strategies will often contribute to the complex contexts in which learners encounter items.

By utilizing interactive stories for classroom and homework exercises and activities, character education programs can be more precisely evaluated.

The modeling and assessment of learners and their progressions, along with the measurement and evaluation of interactive story items, can enhance educators’ understandings of what works and why, allowing educators to continually improve character education exercises, activities, and programs.


Teaching Character and Virtue in Schools by James Arthur, Kristján Kristjánsson, Tom Harrison, Wouter Sanderse and Daniel Wright, An Historical Analysis of Character Education by Michael Watz, Moral Literacy and the Formation of Character by William J. Bennett, Using Interactive Storytelling to Identify Personality Traits by Raul Paradeda, Maria J. Ferreira, Carlos Martinho and Ana Paiva, Player Behavior and Personality Modeling for Interactive Storytelling in Games by Edirlei S. de Lima, Bruno Feijó and Antonio L. Furtado, The Efficient Measurement of Personality: Adaptive Personality Inventories for Survey Research by Josh Cutler and Jacob M. Montgomery, Character Strengths and Virtues: A Handbook and Classification by Christopher Peterson and Martin E. P. Seligman, The Measurement of Moral Judgment: Theoretical Foundations and Research Validation by Anne Colby and Lawrence Kohlberg, Social Intelligence: Measuring the Development of Sociomoral Reflection by John C. Gibbs and Keith F. Widaman, Development in Judging Moral Issues by James R. Rest, “Intermediate” Concepts and the Connection to Moral Education by Muriel J. Bebeau and Stephen J. Thoma, Social Comprehension and Judgment: The Role of Situation Models, Narratives, and Implicit Theories by Robert S. Wyer, Atlas of Moral Psychology edited by Kurt Gray and Jesse Graham, On the Possibility of Intuitive and Deliberative Processes Working in Parallel in Moral Judgment by Kees van den Bos, Towards a Theory of Choice Poetics by Peter Mawhorter, Michael Mateas, Noah Wardrip-Fruin and Arnav Jhala, Introducing PETE: Computer Support for Teaching Ethics by Ilya M. Goldin, Kevin D. Ashley and Rosa L. Pinkus, AEINS: Adaptive Educational Interactive Narrative System to Teach Ethics by Rania Hodhod, Daniel Kudenko and Paul Cairns, Serious Games for Professional Ethics: An Architecture to Support Personalization by Adam McKenzie and Gord McCalla, Supporting Students in the Analysis of Case Studies for Professional Ethics Education by Mayya Sharipova, Automatic Novel Writing: A Status Report by Sheldon Klein, John F. Aeschlimann, David F. Balsiger, Steven L. Converse, Claudine Court, Mark Foster, Robin Lao, John D. Oakley and Joel Smith, Using Planning Structures to Generate Stories by James R. Meehan, The Metanovel: Writing Stories by Computer by James R. Meehan, TALE-SPIN: An Interactive Program That Writes Stories by James R. Meehan, Story Generation after TALE-SPIN by Natlie Dehn, Creating a Story-telling Universe by Michael Lebowitz, Minstrel: A Computer Model of Creativity and Storytelling by Scott R. Turner, MEXICA: A Computer Model of Creativity in Writing by Rafael Pérez y Pérez, Artificial Intelligence and Literary Creativity: Inside the Mind of Brutus, a Storytelling Machine by Selmer Bringsjord and David Ferrucci, Narrative Generation: Balancing Plot and Character by Mark O. Riedl and R. Michael Young, From Linear Story Generation to Branching Story Graphs by Mark O. Riedl and R. Michael Young, Virtual Reality, Art, and Entertainment by Joseph Bates, Computers as Theatre by Brenda Laurel, Hamlet on the Holodeck by Janet H. Murray, Dynamic, User-centered Resolution in Interactive Stories by Nikitas M. Sgouros, Acting in Character by Barbara Hayes-Roth, Robert van Gent and Daniel Huber, Character-based Interactive Storytelling by Marc Cavazza, Fred Charles and Steven J. 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Isbell and Michael Mateas, Interactive Storytelling with Literary Feelings by David Pizzi, Fred Charles, Jean-Luc Lugrin and Marc Cavazza, Interactive Storytelling: A Player Modelling Approach by David Thue, Vadim Bulitko, Marcia Spetch and Eric Wasylishen, Schemas in Directed Emergent Drama by Maria Arinbjarnar and Daniel Kudenko, Generation of Adaptive Dilemma-based Interactive Narratives by Heather Barber and Daniel Kudenko, Chris Crawford on Interactive Storytelling by Chris Crawford, Reader-model-based Story Generation by Peter Mawhorter, A User Model for the Generation of Dilemma-based Interactive Narratives by Heather Barber and Daniel Kudenko, Mimisbrunnur: AI-assisted Authoring for Interactive Storytelling by Ingibergur Stefnisson and David Thue, Automated Structural Analysis of Interactive Narratives by Elin Carstensdottir, Automatic Story Generation: Challenges and Attempts by Amal Alabdulkarim, Siyan Li and Xiangyu Peng, Does Reading Moral Stories Build Character? by Darcia Narvaez, Theoretical Concerns: Vygotsky on Imagination Development by Natalia Gajdamaschko, Survey of Outcomes Measurement in Research on Character Education Programs by Ann E. Person, Emily Moiduddin, Megan Hague-Angus and Lizabeth M. Malone and The Rebirth and Retooling of Character Education in America by Russel J. Sojourner.