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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In the Personal context, reviewing support is typically provided through quantitative feedback, with a focus on evaluation rather than direct revision.
“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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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 · see citing works
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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 · see citing works
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BackgroundMethodInfluentialA Review of Recent Deep Learning Approaches in Human-Centered Machine Learning
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Wambsganß, T., Janson, A., & Leimeister, J. M. (2022) · Computers & Education · doi:10.1016/j.compedu.2022.104644 · Cited by 84 · see citing works
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BackgroundMethodInfluentialSelf-citationBai, X., & Stede, M. (2022) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-022-00323-0 · Cited by 67 · see citing works
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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 78 · see citing works
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MethodFriction: Deciphering Writing Feedback into Writing Revisions through LLM-Assisted Reflection
Zhang, C., Ju, K., Bidoshi, P., Yen, Y., & Rzeszotarski, J. M. (2025) · International Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3714316 · Cited by 46 · see citing works
ReviewFlow: Intelligent Scaffolding to Support Academic Peer Reviewing
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Afrin, T., Kashefi, O., Olshefski, C. A., Litman, D., Hwa, R., & Godley, A. (2021) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445683 · Cited by 44 · see citing works
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BackgroundXNLI: Explaining and Diagnosing NLI-Based Visual Data Analysis
Feng, Y., Wang, X., Pan, B., Wong, K.-K., Ren, Y., Liu, S., Yan, Z., Ma, Y., Qu, H.-M., & Chen, W. (2023) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2023.3240003 · Cited by 42 · see citing works
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Peng, Z., Wang, X., Han, Q., Zhu, J., Ma, X., & Qu, H. (2023) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3586183.3606786 · Cited by 29 · see citing works
Metacognition meets <scp>AI</scp> : Empowering reflective writing with large language models
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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 · see citing works
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Ethical Design of Conversational Agents: Towards Principles for a Value-Sensitive Design
Wambsganß, T., Höch, A., Zierau, N., & Söllner, M. (2021) · Lecture notes in information systems and organisation · doi:10.1007/978-3-030-86790-4_37 · Cited by 31 · see citing works
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Wambsganß, T., Janson, A., Söllner, M., Koedinger, K. R., & Leimeister, J. M. (2024) · Information Systems Research · doi:10.1287/isre.2021.0615 · Cited by 41 · see citing works
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BackgroundInfluentialSelf-citationLearning with Digital Agents: An Analysis based on the Activity Theory
Dolata, M., Katsiuba, D., Wellnhammer, N., & Schwabe, G. (2023) · Journal of Management Information Systems · doi:10.1080/07421222.2023.2172775 · Cited by 37 · see citing works
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Supporting Cognitive and Emotional Empathic Writing of Students
Wambsganss, T., Niklaus, C., Söllner, M., Handschuh, S., & Leimeister, J. (2021) · Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2021.acl-long.314 · Cited by 31 · see citing works
“Individual empathy training is only available for a limited number of students since individual feedback through a student’s learning journey is often hindered due to large-scale lectures or the growing field of distance learning scenarios such as Massive Open Online Classes (MOOCs) (Seaman et al., 2018; Hattie and Ti…”
BackgroundInfluentialSelf-citationA Corpus for Argumentative Writing Support in German
Wambsganss, T., Niklaus, C., Söllner, M., Handschuh, S., & Leimeister, J. (2020) · International Conference on Computational Linguistics · doi:10.48550/arxiv.2010.13674 · Cited by 30
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BackgroundSelf-citationCriTrainer: An Adaptive Training Tool for Critical Paper Reading
Yuan, K., Lin, H., Cao, S., Peng, Z., Guo, Q., & Ma, X. (2023) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3586183.3606816 · Cited by 25 · see citing works
“For each tool, we adopt the technology acceptance model from [65, 81, 84] to measure its usefulness (four items; Cronbach’s 𝛼 = 0.919); easy to use (two items; Cronbach’s 𝛼 = 0.701); and intention to use (two items; Cronbach’s 𝛼 = 0.901).”
MethodInfluentialReviewriter: AI-Generated Instructions For Peer Review Writing
Su, X., Wambsganß, T., Rietsche, R., Neshaei, S. P., & Kser, T. (2023) · Workshop on Innovative Use of NLP for Building Educational Applications · doi:10.18653/v1/2023.bea-1.5 · Cited by 19 · see citing works
Self-citationCoArgue : Fostering Lurkers’ Contribution to Collective Arguments in Community-based QA Platforms
Liu, C., Zhou, S., Liu, D., Li, J., Huang, Z., & Ma, X. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3580932 · Cited by 19 · see citing works
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Gero, K., Calderwood, A., Li, C., & Chilton, L. (2022) · Proceedings of the First Workshop on Intelligent and Interactive Writing Assistants (In2Writing 2022) · doi:10.18653/v1/2022.in2writing-1.2 · Cited by 25 · see citing works
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Self-citationModeling Persuasive Discourse to Adaptively Support Students’ Argumentative Writing
Wambsganss, T., & Niklaus, C. (2022) · Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2022.acl-long.599 · Cited by 25 · see citing works
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Vaithilingam, P., Kim, M. G., Acosta-Parenteau, F.-C., Lee, D. W., Mhedhbi, A., Glassman, E. L., & Arawjo, I. (2025) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3746059.3747778 · Cited by 10 · see citing works
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Self-citationLettersmith: Scaffolding Written Professional Communication Among College Students
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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 · see citing works
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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 18 · see citing works
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A Taxonomy for Deep Learning in Natural Language Processing
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InfluentialKönig, C. M., Karrenbauer, C., & Breitner, M. H. (2022) · Education and Information Technologies · doi:10.1007/s10639-022-11394-w · Cited by 22 · see citing works
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“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., Crowston, K., & de Vreede, G.-J. (2021) · Electronic Markets · doi:10.1007/s12525-021-00481-4 · Cited by 14 · see citing works
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 · see citing works
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MindMate: Exploring the Effect of Conversational Agents on Reflective Writing
Neshaei, S. P., Wambsganss, T., Bouchrifi, H. E., & Käser, T. (2025) · CHI Extended Abstracts · doi:10.1145/3706599.3720029 · Cited by 13 · see citing works
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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 · see citing works
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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 9 · see citing works
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Chen, Q.-Y., Liu, S., Huang, K.-H., Wang, X.-B., Ma, X.-J., Zhu, J., & Peng, Z.-H. (2024) · Conference on Designing Interactive Systems · doi:10.1145/3643834.3661581 · Cited by 11 · see citing works
“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 · see citing works
“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
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“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 · see citing works
“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., Huang, T.-H. '., Kang, D., Lee, M., Raheja, V., & Wambsganss, T. (2023) · CHI Extended Abstracts · doi:10.1145/3544549.3573826 · Cited by 10 · see citing works
“, 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 · see citing works
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 · see citing works
“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 · see citing works
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 · see citing works
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 12 · see citing works
“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., Janson, A., & Wambsganss, T. (2025) · Proc. ACM Hum. Comput. Interact. · doi:10.1145/3711023 · Cited by 6 · see citing works
“…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.-Y., Zeng, J., Wang, J., Liu, J.-Y., Qiu, Y.-H., Yuan, K.-Y., & Peng, Z.-H. (2025) · Proc. ACM Hum. Comput. Interact. · doi:10.1145/3757551 · Cited by 5 · see citing works
“…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 · see citing works
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 · see citing works
Designing Adaptive Argumentation Learning Systems Based on Artificial Intelligence
Wambsganss, T. (2021) · CHI Extended Abstracts · doi:10.1145/3411763.3443422 · Cited by 5 · see citing works
“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 · see citing works
“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 · see citing works
“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 3 · see citing works
(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 · see citing works
“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 3 · see citing works
Mirzababaei, B., & Pammer-Schindler, V. (2024) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2024.3367738 · Cited by 3 · see citing works
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 · see citing works
“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
“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 · see citing works
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 · see citing works
“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 Design
Liu, H., Su, Y.-H., & Lease, M. (2025) · arXiv.org · doi:10.48550/arxiv.2505.09862 · Cited by 2
“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 · see citing works
“…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 · see citing works
“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.-Y., Chen, G.-Z., Liang, S.-Z., Lin, H.-H., Guo, Q.-Y., Liu, D.-D., Ma, X.-J., & Peng, Z.-H. (2026) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3816913 · Cited by 1 · see citing works
“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 · see citing works
“The findings of this study aligned with the previous research [27].”
MethodResultInfluentialSelf-citationRaffaghelli, J., & Crudele, F. (2023) · Media Education · doi:10.36253/me-13801 · Cited by 1 · see citing works
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 · see citing works
“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 · see citing works
“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
- 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
- Physical Sciences › Computer Science › Information Systems › 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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