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AL: An Adaptive Learning Support System for Argumentation Skills
Abstract
Recent advances in Natural Language Processing (NLP) bear the opportunity to analyze the argumentation quality of texts. This can be leveraged to provide students with individual and adaptive feedback in their personal learning journey. To test if individual feedback on students' argumentation will help them to write more convincing texts, we developed AL, an adaptive IT tool that provides students with feedback on the argumentation structure of a given text. We compared AL with 54 students to a proven argumentation support tool. We found students using AL wrote more convincing texts with better formal quality of argumentation compared to the ones using the traditional approach. The measured technology acceptance provided promising results to use this tool as a feedback application in different learning settings. The results suggest that learning applications based on NLP may have a beneficial use for developing better writing and reasoning for students in traditional learning settings.
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“Reviewing support is typically provided through quantitative feedback, with a focus on evaluation rather than direct revision [95,132].”
From Co-Writing with AI, on Human Terms: Aligning Research with User Demands Across the Writing Process (2025) · cites this paper as [132]
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Meyer, J., Jansen, T., Schiller, R., Liebenow, L. W., Steinbach, M., Horbach, A., & Fleckenstein, J. (2024) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2023.100199 · Cited by 311
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“For example, to assist critical writing, designers can add features such as the persuasiveness of the arguments (Wambsganss et al., 2020) and recommended similar arguments from published papers (Peng et al., 2020; Hui et al., 2018).”
InfluentialArgueTutor: An Adaptive Dialog-Based Learning System for Argumentation Skills
Wambsganss, T., Kueng, T., Soellner, M., & Leimeister, J. M. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445781 · Cited by 138
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BackgroundMethodInfluentialSelf-citationBauer, E., Greisel, M., Kuznetsov, I., Berndt, M., Kollar, I., Dresel, M., Fischer, M. R., & Fischer, F. (2023) · British Journal of Educational Technology · doi:10.1111/bjet.13336 · Cited by 130
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BackgroundKnote, R., Janson, A., Söllner, M., & Leimeister, J. M. (2020) · Journal of the Association for Information Systems · doi:10.17705/1jais.00667 · Cited by 103
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“The expert evaluators were given a set of assessment criteria adapted from the official GRE website16, Re3 [83] and Adaptive Learning system [73], where they rated each submission on the following six dimensions on a scale of 1 to 10.”
BackgroundMethodInfluentialA Review of Recent Deep Learning Approaches in Human-Centered Machine Learning
Kaluarachchi, T. I., Reis, A., & Nanayakkara, S. C. (2021) · Sensors · doi:10.3390/s21072514 · Cited by 135
“[128] for students to develop argumentation quality in writing by providing feedback.”
BackgroundMethodInfluentialEnhancing argumentative writing with automated feedback and social comparison nudging
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“In designing the tool, we followed the findings of recent user studies (Afrin et al., 2021; Wambsganss et al., 2020), a review of the extant literature related to the provision of feedback related to failure (e.g., Bjork et al. (2013); Metcalfe (2017); Lawrence and Reed (2019)), and self-regulated…”
BackgroundMethodInfluentialSelf-citationBai, X., & Stede, M. (2022) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-022-00323-0 · Cited by 71
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BackgroundChrysafiadi, K., Virvou, M. K., Tsihrintzis, G. A., & Hatzilygeroudis, I. (2022) · Education and Information Technologies · doi:10.1007/s10639-022-11444-3 · Cited by 80
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BackgroundEffective Interfaces for Student-Driven Revision Sessions for Argumentative Writing
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MethodStoryfier: Exploring Vocabulary Learning Support with Text Generation Models
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Metacognition meets AI : Empowering reflective writing with large language models
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Persua: A Visual Interactive System to Enhance the Persuasiveness of Arguments in Online Discussion
Xia, M., Zhu, Q., Wang, X., Nie, F., Qu, H., & Ma, X. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3555210 · Cited by 39
“This requirement echos one previous research [68].”
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Reza, M., Thomas-Mitchell, J., Dushniku, P., Laundry, N., Williams, J. J., & Kuzminykh, A. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3757566 · Cited by 37
“Finally, reviewing support was limited, and focused on evaluation using quantitative feedback like readability metrics [87], and visual feedback such as progress bars [89].”
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“While a number of well-defined models exist in the field of AM (Freeman, 2001; Perelman, 1971; Pollock, 1995; Walton and Macagno, 2015; Walton, 1996; Wambsganss et al., 2020a), there is no general argumentation annotation scheme across all domains and genres of texts.”
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“gumentative components & relations and providing the evaluation scores [125].”
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“AL (Wambsganss et al., 2020) used natural language processing to provide feedback on the quality of essays in terms of their argument structure, readableness, and coherence.”
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“Inspired by Stab and Gurevych (2017a); Wambsganss et al. (2020c), our guideline consisted of 26 pages, including definitions and rules for what is an ar-gument, which annotation scheme is to be used, and how argument components, argumentative relations, and the qualitative attributes are to be…”
BackgroundMethodInfluentialSelf-citationAdaptive Empathy Learning Support in Peer Review Scenarios
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“Since metacognition skills are complex pedagogical constructs, we relied on a proven method to measure metacognition skills in textual data (e.g., done for argumentation skills in [2, 96, 101] or for problem-solving skills in [103]).”
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“Metacognition is helpful, or even essential, in a wide variety of HCI tasks, such as using computer programming tools [8, 39, 46], writing argumentative text on the web [60], creating and assessing visualizations [33], and learning via technology [4].”
BackgroundArgueBot: A Conversational Agent for Adaptive Argumentation Feedback
Wambsganß, T., Guggisberg, S., & Söllner, M. (2021) · Lecture notes in information systems and organisation · doi:10.1007/978-3-030-86797-3_18 · Cited by 20
Self-citationWambsganss, T., Benke, I., Maedche, A., Koedinger, K. R., & Käser, T. (2024) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-024-00409-x · Cited by 20
Self-citationLettersmith: Scaffolding Written Professional Communication Among College Students
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“Recent advances in writing instruction tools have started to support writing structure and organization by demonstrating the use of machine learning in guiding how to write an argumentative essay [92, 94] and evaluating whether a learner has succeeded or failed at particular rhetorical moves [93, 95, 96].”
BackgroundImproving Students Argumentation Learning with Adaptive Self-Evaluation Nudging
Wambsganß, T., Janson, A., Käser, T., & Leimeister, J. M. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3555633 · Cited by 20
“Thus, research in fields such as HCI or educational technology has shown a growing interest in developing argumentation systems to support individuals [2, 26, 110, 113].”
BackgroundMethodInfluentialSelf-citationYou’ve Got AI Friend in Me: LLMs as Collaborative Learning Partners
Oppenheimer, D. M., Cash, T. N., & Pensky, A. C. (2025) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-025-00521-6 · Cited by 21
“Advanced technologies can also be used as argumentative writing tutors that aid students in crafting arguments (Hoffmann, 2015) and provide adaptive feedback on the quality of arguments generated by students (Scheuer et al., 2010; Wambsganss et al., 2020).”
A Taxonomy for Deep Learning in Natural Language Processing
Landolt, S., Wambsganss, T., & Söllner, M. (2021) · Hawaii International Conference on System Sciences · doi:10.24251/hicss.2021.129 · Cited by 18
“, to enhance individual learning of students with adaptive writing support systems [8] or adaptive tutoring through a smart personal assistant [9].”
BackgroundSelf-citationWhich Side Are You On? A Multi-task Dataset for End-to-End Argument Summarisation and Evaluation
Li, H., Wu, Y., Schlegel, V., Batista-Navarro, R., Madusanka, T., Zahid, I., Zeng, J., Wang, X., He, X., Li, Y., et al. (2024) · Research Explorer (The University of Manchester) · doi:10.48550/arxiv.2406.03151 · Cited by 2
Kerruish, E. (2024) · Postdigital Science and Education · doi:10.1007/s42438-024-00456-6 · Cited by 16
“The problems and constraints encountered in employing assorted and layered digital critical thinking teaching technologies are often thought to result from the people using the technologies (students, teachers and institutions) (for example, Arend ; Wambsganss et al. 2020).”
InfluentialKönig, C. M., Karrenbauer, C., & Breitner, M. H. (2022) · Education and Information Technologies · doi:10.1007/s10639-022-11394-w · Cited by 23
“…have already introduced many different assistants, which, for example, support students to learn to write program code, strengthen their argumentation skills, answer FAQs, or support study course selections (Bouaiachi et al., 2014; Hobert, 2019; Ranoliya et al., 2017; Wambsganss et al., 2020).”
BackgroundWeber, F., Wambsganß, T., Neshaei, S. P., & Söllner, M. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.145 · Cited by 11
“One promising solution to better support students in their writing process and to overcome the limitations in law courses would be the use of writing support systems that could provide individualized feedback to students (Wambsganss et al., 2020a).”
BackgroundSelf-citationMirzababaei, B., & Pammer-Schindler, V. (2021) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2021.645516 · Cited by 14
“These contributions complement existing research that has worked on longer argumentative essays (Wambsganss et al., 2020), which has differently conceptualized argumentation quality that is however less suitable for direct feedback within a conversational agent, and broader work on argumentation mining on identifying…”
BackgroundHybrid intelligence in business networks
Ebel, P., Söllner, M., Leimeister, J. M., Crowston, K., & de Vreede, G. (2021) · Electronic Markets · doi:10.1007/s12525-021-00481-4 · Cited by 20
Self-citationSynthia: Visually Interpreting and Synthesizing Feedback for Writing Revision
Zhang, C., Ju, K., Han, Z., Yen, Y., & Rzeszotarski, J. M. (2025) · ACM Symposium on User Interface Software and Technology · doi:10.1145/3746059.3747703 · Cited by 13
“For instance, AL [70] visualizes argumentative sentence relationships and scores persuasiveness, while ArgRewrite [84] tracks and assesses sentence-level changes.”
MindMate: Exploring the Effect of Conversational Agents on Reflective Writing
Neshaei, S. P., Wambsganß, T., Bouchrifi, H. E., & Käser, T. (2025) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3706599.3720029 · Cited by 14
“The dashboard was inspired by similar writing assistant works [30, 39, 48, 57, 60, 62] and was developed using the third-party code scripts available in the Spacy and NLTK code libraries 5 .”
Self-citationFu, W.-S., Zhang, J., Zhang, D., Li, T., Lan, M., & Liu, N. (2025) · Journal of Educational Computing Research · doi:10.1177/07356331241313126 · Cited by 15
“Barrot (2023) and Wambsganss et al. (2020) both signi fi cantly improved students ’ writing abilities and promoted the development of their attention and reasoning skills by providing adaptive feedback during the writing process.”
Enhancing Procedural Writing Through Personalized Example Retrieval: A Case Study on Cooking Recipes
Mejia‐Domenzain, P., Frej, J., Neshaei, S. P., Mouchel, L., Nazaretsky, T., Wambsganß, T., Bosselut, A., & Käser, T. (2024) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-024-00405-1 · Cited by 10
Self-citationYamamoto, Y. (2024) · Frontiers in Psychology · doi:10.3389/fpsyg.2024.1382234 · Cited by 12
RetAssist: Facilitating Vocabulary Learners with Generative Images in Story Retelling Practices
Chen, Q., Liu, S., Huang, K., Wang, X., Ma, X., Zhu, J., & Peng, Z. (2024) · Designing Interactive Systems Conference · doi:10.1145/3643834.3661581 · Cited by 21
“For each system interface, we adapt the technology acceptance model [67, 70] to measure the perceived usefulness (four items, e.g., “I fnd the vocabulary learning support system useful in my vocabulary learning process by story retelling”; Cronbach’s = 0.921); easiness to use (four items, e.g., “My…”
MethodInfluentialLiu, Y., Xiong, W., Xiong, Y., & Wu, Y.-F. B. (2023) · Journal of Computers in Education · doi:10.1007/s40692-023-00261-3 · Cited by 13
“In another study, an adaptive learning support system, ‘AL’, designed and implemented by Wambsganss et al. (2020) is capable of using NLP and machine learning to give feedback to students to improve their ability in argumentative writing skills in German language; AL points out which claims are not…”
Teach Me How to Argue: A Survey on NLP Feedback Systems in Argumentation
Guerraoui, C., Reisert, P., Inoue, N., Mim, F. S., Singh, K., Choi, J., Robbani, I., Naito, S., Wang, W., & Inui, K. (2023) · Workshop on Argument Mining (ArgMining) · doi:10.18653/v1/2023.argmining-1.3 · Cited by 9
“Although Wambsganß et al. (2020) and Wamb-sganss et al. (2022a) propose different granularity levels of explanations, their study is restrained to students from their university.”
BackgroundMethodXu, S.-L., Hu, D., Wang, R., & Zhang, X.-Y. (2025) · International Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713480 · Cited by 10
“Unlike AI-generated feedback (CodeTailor[60],AL [140],Surgment [141], and many other systems [31, 71, 84, 106, 123, 129]), our design leverages peer gaze focus aligned with course materials, inspired by peer effect theory showing students’ behaviors are influenced by peers [18, 38, 47, 59, 111,…”
Stahl, M., Düsterhus, N., Chen, M.-H., & Wachsmuth, H. (2023) · doi:10.48550/arxiv.2310.18098 · Cited by 10
“Our proposed enthymeme-related tasks complement these attempts, having the same ultimate goal, namely to learn support systems for argumentative writing (Wambsganss et al., 2020).”
The Second Workshop on Intelligent and Interactive Writing Assistants
Chang, M., Chung, J. J. Y., Gero, K. I., Huang, T.-H., Kang, D., Lee, M., Raheja, V., & Wambsganß, T. (2023) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3544549.3573826 · Cited by 12
“, general essay writing [7], argumentation [9], or writing styles), our understanding is yet limited regarding the granular effects of different writing support modalities (e.”
BackgroundSelf-citationBias at a Second Glance: A Deep Dive into Bias for German Educational Peer-Review Data Modeling
Wambsganss, T., Swamy, V., Rietsche, R., & Käser, T. (2022) · International Conference on Computational Linguistics · doi:10.48550/arxiv.2209.10335 · Cited by 10
“, for argumentation skill training (Wambsganss et al., 2020a) or empathy skills (Wambsganss et al.”
InfluentialSelf-citationDesigning an Adaptive Empathy Learning Tool
Wambsganss, T., Weber, F., & Söllner, M. (2021) · Wirtschaftsinformatik · doi:10.1007/978-3-030-86790-4_31 · Cited by 10
Self-citationNeshaei, S. P., Rietsche, R., Su, X., & Wambsganss, T. (2024) · Proceedings of the 29th International Conference on Intelligent User Interfaces · doi:10.1145/3640543.3645169 · Cited by 9
“The two pre-survey constructs we chose have been employed in prior studies focusing on user-centric systems [101, 102] to check for valid randomization.”
BackgroundMethodSelf-citationCoCo Matrix: Taxonomy of Cognitive Contributions in Co-writing with Intelligent Agents
Wan, R., Gebreegziabher, S. A., Li, T. J.-J., & Badillo-Urquiola, K. (2024) · Creativity and Cognition · doi:10.1145/3635636.3664260 · Cited by 9
ALEN App: Argumentative Writing Support To Foster English Language Learning
Wambsganss, T., Caines, A., & Buttery, P. (2022) · Workshop on Innovative Use of NLP for Building Educational Applications · doi:10.18653/v1/2022.bea-1.18 · Cited by 8
Self-citationKarrenbauer, C., Brauner, T., König, C. M., & Breitner, M. H. (2023) · Educational Technology Research and Development · doi:10.1007/s11423-023-10255-8 · Cited by 16
“Pedagogical conversational agents’ functions and purposes have been manifold and include developing argumentation skills (Wambsganss et al., 2020, 2021), idea generation (Vladova et al., 2019), math education (Cai et al., 2021), programming skills (Hobert, 2019b), information acquisition (Meyer von…”
BackgroundNeshaei, S. P., Tolzin, A., Berkle, Y., Leuchter, M., Leimeister, J. M., Janson, A., & Wambsganß, T. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3711023 · Cited by 7
“…based on the learning from errors paradigm, as well as investigating how more recent NLP models (e.g., GPT) are able to be effective in teaching argumentation skills to learners, compared to the classifier-based approaches with task-specific models done in previous works [135, 139, 140].”
InfluentialSelf-citationPan, Q., Zeng, J., Wang, J., Liu, J., Qiu, Y., Yuan, K., & Peng, Z. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3757551 · Cited by 5
“…trending (“hot") posts ( Range from 2024.8.18 to 2024.8.25 ) as our source data for data annotation for two primary reasons: (1) the platform’s established reputation in facilitating structured argumentation, as evidenced by its extensive use in prior research on online deliberation [59, 79, 82].”
InfluentialGELEX: Generative AI-Hybrid System for Example-Based Learning
Yazici, A., Meija-Domenzain, P., Frej, J., & Käser, T. (2024) · CHI Extended Abstracts · doi:10.1145/3613905.3650900 · Cited by 5
Understanding Revision Behavior in Adaptive Writing Support Systems for Education
Mouchel, L., Wambsganss, T., Mejia-Domenzain, P., & Käser, T. (2023) · Educational Data Mining · doi:10.5281/zenodo.8115766 · Cited by 5
“While prior works on text revision [8, 15, 20, 28] have proposed machine collaborative writing interfaces, they focus on collecting human-machine interaction data to better train neural models, rather than understanding the underlying processes of text revision.”
BackgroundSelf-citationTYPIC: A Corpus of Template-Based Diagnostic Comments on Argumentation
Naito, S., Sawada, S., Nakagawa, C., Inoue, N., Yamaguchi, K., Shimizu, I., Mim, F. S., Singh, K., & Inui, K. (2022) · International Conference on Language Resources and Evaluation · doi:10.48550/arxiv.2201.06674 · Cited by 5
“Wambsganss et al. (2020) proposed a support system that analyzes an argumentation made by learners, displays the argumentation structure, and highlights unsupported claims.”
BackgroundA Cognitive Diagnosis Method in Adaptive Learning System Based on Preconceptions
Wang, J., & Liang, K. (2022) · Scientific Programming · doi:10.1155/2022/5011804 · Cited by 3
Designing Adaptive Argumentation Learning Systems Based on Artificial Intelligence
Wambsganss, T. (2021) · CHI Extended Abstracts · doi:10.1145/3411763.3443422 · Cited by 5
“5 learning systems and the way students want to learn and improve their argumentation skills (more information in [40]).”
BackgroundMethodInfluentialSelf-citationDesign and Evaluation of an Adaptive Empathy Learning Tool
Wambsganss, T., Weber, F., & Söllner, M. (2021) · Hawaii International Conference on System Sciences · doi:10.24251/hicss.2021.007 · Cited by 5
“The potential of student peer feedbacks and metacognition skill training has been successfully shown for other skill training based on NLP such as argumentations skills [43].”
BackgroundSelf-citationBucher, A., Schenk, B., Dolata, M., & Schwabe, G. (2024) · arXiv · doi:10.48550/arxiv.2405.15561 · Cited by 4
“PCAs are effective in a variety of educational contexts, e.g., to support the development of better argumentation and reasoning skills (Wambsganss et al., 2020, 2021), to enhance students’ problem-solving skills (Winkler et al., 2019), or to teach programming skills with a scaffolding-based PCA…”
BackgroundSystematic review on the current state of computer-supported argumentation learning systems
Sinikallio, L., Aunimo, L., & Männistö, T. (2024) · Information and Software Technology · doi:10.1016/j.infsof.2024.107598 · Cited by 5
(X)AI as a Teacher: Learning with Explainable Artificial Intelligence
Spitzer, P., Goutier, M., Kühl, N., & Satzger, G. (2024) · Mensch und Computer (MuC) · doi:10.1145/3670653.3677504 · Cited by 2
“Moreover, with the importance of feedback in learning processes [8, 47], recent advances in this field utilize explainable AI (XAI) to provide additional explanations on top of pure training samples to achieve better learning performances [14].”
Gebbing, P., Khosrawi-Rad, B., Strohmann, T., Windolf, C., & Lattemann, C. (2023) · Bled eConference · doi:10.18690/um.fov.6.2023.13 · Cited by 2
Mirzababaei, B., & Pammer‐Schindler, V. (2024) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2024.3367738 · Cited by 5
A Teaching Framework for the Methodically Versatile DSR Education of Master’s Students
Schlimbach, R., Khosrawi-Rad, B., Grogorick, L., Becker, F., Strohmann, T., Siemon, D., & Robra-Bissantz, S. (2023) · Journal of Information Systems Education · doi:10.62273/wzzc1532 · Cited by 3
“Each mode of the ICAP framework involves different types of behaviors and learning processes that imply learning outcomes augmenting the engagement level (Chi & Wylie, 2014; Wambsganss et al., 2020).”
Elborolosy, S. A. M., & Almujali, R. A. (2020) · Open Journal of Modern Linguistics · doi:10.4236/ojml.2020.103010 · Cited by 3
Adaptive Tutoring Modalities for Supporting Learners’ Reflective Writing Practices
Wettstein, L., Neshaei, S. P., Rietsche, R., & Wambsganß, T. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790599 · Cited by 1
“This builds upon prior work that has already demonstrated the benefits of traditional writing assistants, which feature a text area and feedback module as their primary interface elements [39, 77].”
Self-citationPutra, M. J. A., & Mailinda, D. (2025) · Jurnal Pendidikan MIPA · doi:10.23960/jpmipa.v26i3.pp1870-1892 · Cited by 2
“This makes TAP an adaptive tool in evaluating argumentative skills according to students' cognitive levels (Wambsganss et al., 2020).”
Huang, Y., Palermo, C., & Wilson, J. (2025) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2025.100479 · Cited by 2
AI-Based Real-Time Feedback System for Improving English Oral Proficiency
Zhou, Y., He, Y., Wang, X.-H., Song, L.-J., Liu, X.-Y., & Liu, W. (2025) · 2025 International Conference on Distance Education and Learning (ICDEL) · doi:10.1109/icdel65868.2025.11193512 · Cited by 2
“By interweaving pronunciation, grammatical, and pragmatic analysis, it charts a comprehensive route to balanced communicative competence [13].”
Rhetorical XAI: Explaining AI's Benefits as well as its Use via Rhetorical DesignPreprint
Liu, H., Su, Y., & Lease, M. (2025) · arXiv (Cornell University) · doi:10.48550/arxiv.2505.09862 · Cited by 4
“We assess each strategy’s strengths and limitations based on our axial coding process, linking reported user experiences to their intended rhetorical appeals.”
Teach Me How to Improve My Argumentation Skills: A Survey on Feedback in Argumentation
Guerraoui, C., Reisert, P., Inoue, N., Mim, F. S., Naito, S., Choi, J., Robbani, I., Wang, W., & Inui, K. (2023) · arXiv.org · doi:10.48550/arxiv.2307.15341 · Cited by 2
Learning First-Order Logic Rules for Argumentation Mining
Sun, Y., Chen, G., Alinejad‐Rokny, H., Bao, J., Huang, Y., Liang, B., Wong, K., Yang, M., & Xu, R. (2025) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2025.acl-long.691 · Cited by 1
“…and has been shown to provide support across various domains, such as decision-making (Vassiliades et al., 2021; Walker et al., 2018), writing support (Wamb-sganss and Rietsche, 2019; Wambsganss et al., 2020), and argument generation (Khatib et al., 2021; Hua et al., 2019; Slonim et al., 2021).”
Burkhard, M., Seufert, S., Gubelmann, R., Niklaus, C., & Panjaburee, P. (2023) · International Conference on Computer Supported Education · doi:10.5220/0011984100003470 · Cited by 2
“The tool Artist is an adapted version based on the tool Argumentation Learning developed at the University of St.Gallen by Wambsganss et al. (2020).”
InfluentialSelf-citationMeyer, J., Steinbach, M., Schiller, R., Mertens, U., Schaller, N.-J., Horbach, A., Fleckenstein, J., Köller, O., Kizilcec, R. F., & Jansen, T. (2026) · ACM Conference on Learning @ Scale (L@S) · doi:10.1145/3774398.3811602 · Cited by 1
“However, providing individualized, high-quality essay feedback is time-consuming [29, 99, 108], thus limiting teachers’ capacity to provide detailed input to all students and thereby reducing opportunities for effective practice [26].”
Yuan, K., Chen, G., Liang, S., Lin, H., Guo, Q., Liu, D., Ma, X., & Peng, Z. (2026) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3816913 · Cited by 1
“For each tool, we adopt the technology acceptance model from [85, 104, 110] to measure its usefulness (four items; Cronbach’s 𝛼 = 0.950 ), ease of use (four items; Cronbach’s 𝛼 = 0.948 ), and intent to use (two items; Cronbach’s 𝛼 = 0.954 ).”
InfluentialInstructional Mechanisms for Professional Writing: A Comparison of Scaffolded Annotation and ChatGPT
Corvite, S., Matz, R. L., Mills, M., & Hui, J. S. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790915
“More recently, HCI researchers have examined the use of machine learning and GenAI as tools for supporting students’ writing, such as to guide the composition of argumentative essays [108, 110] and assess whether a learner has effectively executed specific rhetorical strategies [109, 111, 112].”
Pereira, D. E., Simão, D. T., & Campelo, C. E. C. (2025) · Brazilian Symposium in Information and Human Language Technology · doi:10.5753/stil.2025.37836 · Cited by 1
“…has focused on automatically identifying and analyzing argu-mentative structures in diverse texts, including essays, [Sazid and Mercer 2022, Wambsganss et al. 2020a, Abkenar et al. 2021, Guo et al. 2024], scientific papers [Michael, Fromm et al. 2020, Al Khatib et al. 2021, Stylianou and…”
Pereira, D. E., Gomes, D., Vasconcelos, L. L., & Campelo, C. E. C. (2025) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.70041 · Cited by 1
“In order to effectively address RQ 3, it is crucial to emphasize the tool's positive impact on the development of students' writing skills within the scholarly environment, according to feedback (technology acceptance metric) collected from students (Wambsganss et al. 2020).”
Mehrebenenannotation argumentativer Lerner*innentexte für die automatische Textauswertung
Kilsbach, S., Rezat, S., Michel, N., Karabey, R., Stahl, M., & Wachsmuth, H. (2025) · Zeitschrift für Angewandte Linguistik · doi:10.1515/zfal-2025-2003 · Cited by 1
Baskara, R. (2024) · Abjadia International Journal of Education · doi:10.18860/abj.v9i1.18795 · Cited by 1
“These strategies can provide learners with feedback and guidance on their language use, help track their progress, and identify areas for improvement (Wambsganss et al., 2020).”
BackgroundArgumentative Writing Software: Perceptions of Undergraduate Students toward Artist Prototype
Htaw, M. C., Pipa, D., Sriwattanarothai, N., Pichitpornchai, C., Gubelmann, R., Seufert, S., Niklaus, C., & Handschuh, S. (2024) · IEEE Eurasian Conference on Educational Innovation (ECEI) · doi:10.1109/ecei60433.2024.10510808 · Cited by 1
“The findings of this study aligned with the previous research [27].”
MethodResultInfluentialSelf-citationRaffaghelli, J. E., & Crudele, F. (2023) · Media Education · doi:10.36253/me-13801 · Cited by 6
Le mappe argomentative come comparator di feedback interno: un laboratorio per studenti universitari
Crudele, F., & Raffaghelli, J. (2023) · EDUCATION SCIENCES AND SOCIETY · doi:10.3280/ess1-2023oa15297 · Cited by 1
“For terms and conditions of usage please see: http://creativecommons.org 225 un valido supporto all’apprendimento del procedere argomentativo (Wambsganss et al., 2020).”
BackgroundInfluentialSciNote: Collaborative Problem Solving and Argumentation Tool
Rafner, J., Hjorth, A., Weidner, C., Ahmed, S. Z., Poulsen, C., Klokmose, C., & Sherson, J. (2021) · arXiv.org · doi:10.48550/arxiv.2105.06269 · Cited by 1
Developing Written Argumentation Skills With an Educational Simulation Game (ESG)
Riel, J., & Lawless, K. A. (2022) · Advances in mobile and distance learning book series · doi:10.4018/978-1-7998-9004-1.ch007
Analysis of Trends in Adaptive Learning Using Google Trends and Bibliometric
Kurniawan, C., & Kusumaningrum, S. R. (2021) · Advances in Social Science, Education and Humanities Research · doi:10.2991/assehr.k.211210.015 · Cited by 1
“The application of adaptive learning provides feedback based on the learners' learning path [5].”
BackgroundXia, D., Casal, J. E., Purwanto, E., & Atmadja, N. (2026) · System · doi:10.1016/j.system.2026.104169
Metadata
- Authors
- Wambsganss, T., Niklaus, C., Cetto, M., Söllner, M., Handschuh, S., & Leimeister, J. M.
- Affiliations
- University of St.Gallen (CH)
- University of Kassel (DE)
- University of Passau (DE)
- Venue
- Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2020-04-21
- Pages
- 1-14
- OpenAlex
- W3028947997
- Cited by
- 101
- References
- 0
- Citations by year
- 2026: 12 · 2025: 19 · 2024: 19 · 2023: 24 · 2022: 12 · 2021: 14 · 2020: 1
- FWCI
- 4.86
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion)
- Alexandria (UniSG) (University of St.Gallen) (acceptedVersion)
- Alexandria (UniSG) (University of St.Gallen) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Social Sciences › Education › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Keywords
- argumentation skills, individual feedback, argumentation structure, argumentative writing, technology acceptance, writing instruction, reasoning skills
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