Phoster

Research and Development

Self-improving Adaptive Instructional Systems Capable of Generating and Discussing Moral Stories

Introduction

Self-improving adaptive instructional systems can select and generate moral stories, scenarios, or cases to discuss with individuals and teams. Man-machine literature discussions about moral stories could provide value both to individuals (Goldenberg, 1992) and to artificial-intelligence systems (Sinatra, Graesser, Hu, Brawner, & Rus, 2019; Tong & Hu, 2024).

User Modeling

Self-improving adaptive instructional systems model users, both learners and experts. These models can be more generic, or stereotype-based, or more specific and highly adaptive.

User models can be of use for intelligently selecting, generating, distributing, and administering moral stories across populations to maximize value both for users and artificial-intelligence systems.

Literature Discussions

Beyond presenting users with adaptive and personalized sequences of questions about moral stories, artificial-intelligence systems can, increasingly, participate in more interesting, engaging, and enriching man-machine literature discussions.

While the discourse of reading groups has previously been explored (Peplow, Swann, Trimarco, & Whitely, 2015), man-machine literature discussions and co-reading are comparably new terrain.

To increase the value of resultant educational data, techniques from opinion polling, survey design, and questionnaire construction could be of use during literature discussions. Pertinent topics would include avoiding leading questions or loaded questions, and having a mindfulness of the framing of questions, context effects, and item-sequencing effects.

Agentic Workflow

When artificial-intelligence systems generate moral stories, they could also generate agentic workflows, or "scripts", describing the processes with which to discuss the stories and how to intersperse questions or testlets. Discussion questions for readers can be presented to them in the middles of reading moral stories, e.g., at section or chapter boundaries, and upon stories' completions.

Agentic workflows, or "scripts", can include branching points. There could be multiple paths available both through them and accompanying testlets.

Story Generation

Artificial-intelligence systems can generate stories in order to accomplish specified pedagogical objectives. Specified pedagogical objectives should be preserved and accompany generated story items, alongside other artifacts produced during story generation, as metadata to simplify story understanding, analysis, and evaluation.

Descriptions of intended audiences can also be provided to story generators. These input data would allow generated moral stories to be developmentally appropriate with respect to their subject matter, grammar, and vocabulary (Valentini, Weber, Salcido, Wright, Colunga, & Kann, 2023).

Story Understanding

Moral stories present readers with situations in story contexts about which moral reasoning and discussion occur. Meanwhile, values can be both general and context-specific with respect to alignment (Liscio, van der Meer, Siebert, Jonker, & Murukannaiah, 2022).

Components could be created for modeling aspects of story comprehension during, in the middles of, unfolding stories. Predictions, then, could be made with respect to readers' responses to those moral situations occurring in those contexts presented by stories.

Story Analysis

Different stories about identical moral themes can cause different distributions of responses and discussions.

Over the course of time, provided with adequate data, artificial-intelligence systems could discern and learn causal relationships between the types, meanings, structures, devices, forms, and effects of moral stories.

Self-improvement

Artificial-intelligence systems can self-improve with respect to both the generation and execution of agentic workflows, or "scripts".

Components for the generation, understanding, analysis, and evaluation of moral stories can self-improve. In these regards, perhaps forms of A/B and multivariate testing could occur as systems exploited and explored variations in moral stories and literature discussions to achieve pedagogical objectives.

Alignment

In artificial intelligence, value-alignment challenges include how to align artificial-intelligence systems to sets of values and how to determine which to do so for (Gabriel, 2020).

Artificial-intelligence systems can learn from and be aligned to values from moral stories (Riedl & Harrison, 2016; Emelin, Le Bras, Hwang, Forbes, & Choi, 2020; Nahian, Tasrin, Frazier, Riedl, & Harrison, 2025).

Man-machine literature discussions about selected or generated moral stories can also provide value to artificial-intelligence systems. Research is underway into mining human tutorial discussions (Maharjan, Rus, & Gautam, 2018; Lin, Singh, Sha, Tan, Lang, Gašević, & Chen, 2022) and these techniques will be increasingly useful for analyzing and learning from transcripts of man-machine discussions.

Artificial-intelligence systems will be able to select and generate moral stories to engage in man-machine literature discussions, continuously learning from experts while tutoring learners.

Pluralism

Instead of attempting to train a morally absolutist artificial-intelligence system, systems could be trained to be increasingly capable of adopting a variety of ideological stances, positions, perspectives, schools of thought, and wisdom traditions. Resultant pluralist systems could, then, be prompted to perform moral reasoning and to engage in dialogue from described personas (Shanahan, McDonell, & Reynolds, 2023; Kovač, Portelas, Sawayama, Dominey, & Oudeyer, 2024).

In addition to single artificial-intelligence systems capable of performing many personas, components can be envisioned which can route descriptions of personas to those other models most capable of performing them.

Multi-agent Systems

Multi-agent systems can be of use for: intelligent tutoring (Šarić-Grgić, Grubišić, Stankov, & Štula, 2019); representing personas capable of performing reasoning and dialogue from differing ideological stances, positions, perspectives, schools of thought, and wisdom traditions; contextual value alignment (Dognin, Rios, Luss, Padhi, Riemer, Liu, Sattigeri, Nagireddy, Varshney, & Bouneffouf, 2024); story generation (Huot, Amplayo, Palomaki, Jakobovits, Clark, & Lapata, 2024); literature discussions; and otherwise modeling and simulating both learners and experts.

System Operations

Teams of specialized technical personnel could operate self-improving adaptive instructional systems, review automatically-generated story items, monitor unfolding performance metrics pertaining to story items, and monitor real-time analytics dashboards pertaining to the administering of testlets and to the literature discussions between artificial-intelligence systems and populations of learners and experts.

Learners' parents, teachers, teaching assistants, guidance counselors, and school administrators could be provided with means of engaging with educational artificial-intelligence systems, e.g., using multimodal dialogue enhanced by data visualizations and analytics dashboards.

Conclusion

Self-improving adaptive instructional systems can select and generate moral stories, scenarios, or cases to discuss with individuals and teams. Man-machine literature discussions about moral stories could provide value both to individuals and to artificial-intelligence systems.

Artificial-intelligence systems will be able to select and generate moral stories to engage in man-machine literature discussions, continuously learning from experts while tutoring learners.

One secondary benefit of the architectural approaches considered and discussed above is that, with the same components, users would be able to narrate real-world or hypothetical scenarios to artificial-intelligence systems, these serving as the stories for discussion, and to select or describe personas to interact with.

Bibliography

Dognin, Pierre, Jesus Rios, Ronny Luss, Inkit Padhi, Matthew D. Riemer, Miao Liu, Prasanna Sattigeri, Manish Nagireddy, Kush R. Varshney, and Djallel Bouneffouf. "Contextual moral value alignment through context-based aggregation." arXiv preprint arXiv:2403.12805 (2024).

Emelin, Denis, Ronan Le Bras, Jena D. Hwang, Maxwell Forbes, and Yejin Choi. "Moral stories: Situated reasoning about norms, intents, actions, and their consequences." arXiv preprint arXiv:2012.15738 (2020).

Gabriel, Iason. "Artificial intelligence, values, and alignment." Minds and Machines 30, no. 3 (2020): 411-437.

Goldenberg, Claude. "Instructional conversations: Promoting comprehension through discussion." The Reading Teacher 46, no. 4 (1992): 316-326.

Huot, Fantine, Reinald Kim Amplayo, Jennimaria Palomaki, Alice Shoshana Jakobovits, Elizabeth Clark, and Mirella Lapata. "Agents' room: Narrative generation through multi-step collaboration." arXiv preprint arXiv:2410.02603 (2024).

Kovač, Grgur, Rémy Portelas, Masataka Sawayama, Peter Ford Dominey, and Pierre-Yves Oudeyer. "Stick to your role! Stability of personal values expressed in large language models." arXiv preprint arXiv:2402.14846 (2024).

Lin, Jionghao, Shaveen Singh, Lele Sha, Wei Tan, David Lang, Dragan Gašević, and Guanliang Chen. "Is it a good move? Mining effective tutoring strategies from human–human tutorial dialogues." Future Generation Computer Systems 127 (2022): 194-207.

Liscio, Enrico, Michiel T. van der Meer, Luciano C. Siebert, Catholijn M. Jonker, and Pradeep K. Murukannaiah. "What values should an agent align with? An empirical comparison of general and context-specific values." Autonomous Agents and Multi-Agent Systems (2022).

Maharjan, Nabin, Vasile Rus, and Dipesh Gautam. "Discovering effective tutorial strategies in human tutorial sessions." In The Thirty-First International Flairs Conference (2018).

Nahian, Md Sultan Al, Tasmia Tasrin, Spencer Frazier, Mark Riedl, and Brent Harrison. "The Goofus & Gallant story corpus for practical value alignment." arXiv preprint arXiv:2501.09707 (2025).

Peplow, David, Joan Swann, Paola Trimarco, and Sara Whiteley. The Discourse of Reading Groups: Integrating Cognitive and Sociocultural Perspectives. Routledge, (2015).

Riedl, Mark O., and Brent Harrison. "Using stories to teach human values to artificial agents." In Workshops at the Thirtieth AAAI Conference on Artificial Intelligence (2016).

Šarić-Grgić, Ines, Ani Grubišić, Slavomir Stankov, and Maja Štula. "An agent-based intelligent tutoring systems review." International Journal of Learning Technology 14, no. 2 (2019): 125-140.

Shanahan, Murray, Kyle McDonell, and Laria Reynolds. "Role-play with large language models." Nature 623, no. 7987 (2023): 493-498.

Sinatra, Anne M., Arthur C. Graesser, Xiangen Hu, Keith Brawner, and Vasile Rus, eds. Design Recommendations for Intelligent Tutoring Systems: Volume 7 - Self-improving Systems. US Army Research Laboratory, (2019).

Tong, Richard Jiarui, and Xiangen Hu. "Future of education with neuro-symbolic AI agents in self-improving adaptive instructional systems." Frontiers of Digital Education 1, no. 2 (2024): 198-212.

Valentini, Maria, Jennifer Weber, Jesus Salcido, Téa Wright, Eliana Colunga, and Katharina Kann. "On the automatic generation and simplification of children's stories." arXiv preprint arXiv:2310.18502 (2023).