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Education Sciences
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Trends in Teaching and Learning Technologies for Enhancing Assessment and...
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Trends in Teaching and Learning Technologies for Enhancing Assessment and Feedback

A Special Issue of Education Sciences (ISSN 2227-7102) belonging to the section "Technology Enhanced Education".

Deadline for manuscript submissions: closed (31 July 2026) | Viewed by 6357

Editors

Dr. Laura Screpanti

E-Mail Website
Guest Editor
Dipartimento di Scienze della Formazione, dei Bei Culturali e del Turismo, Università degli Studi di Macerata, 62100 Macerata, Italy
Interests: educational robotics; research methodologies; data analysis
Prof. Dr. Lorella Giannandrea

E-Mail Website
Guest Editor
Dipartimento di Scienze della Formazione, dei Bei Culturali e del Turismo, Università degli Studi di Macerata, 62100 Macerata, Italy
Interests: technology for education; self-assessment; assessment; rubrics; ePortfolios

Special Issue Information

Dear Colleagues,

The rapid advancements in teaching and learning technologies are reshaping how educators design assessment and deliver feedback, which is vital for effective learning. Feedback serves as a cornerstone for guiding learners, fostering engagement, and improving outcomes. Technologies such as artificial intelligence, data analytics, and gamification offer unprecedented potential to enhance feedback by making it more personalized, timely, and impactful. However, their successful implementation depends on aligning these innovations with sound pedagogical principles, creating a cohesive approach that integrates technology and teaching methodologies.

This Special Issue explores how the integration of technology and pedagogy can transform assessment and feedback practices in education. It seeks to highlight cutting-edge research, innovative tools, and strategies that demonstrate the seamless collaboration of teaching methods and technological advancements. By bringing together researchers, educators, and practitioners, this Special Issue aims to showcase impactful practices, share insights on effective implementation, and critically examine how technology, when harmonized with pedagogy, can reshape the way feedback supports learning.

We invite submissions of original research articles, reviews, and case studies that present innovative tools, practices, and results. Submissions may address, but are not limited to, the following themes:

  • Innovative digital tools and platforms for assessment and feedback;
  • Artificial intelligence and machine learning applications in education;
  • Data analytics and learning dashboards for personalized feedback;
  • Digital twins and cyber-physical systems in education;
  • Designing equitable and inclusive assessment technologies ;
  • Case studies on the implementation of technology-enhanced assessment practices;
  • Challenges and barriers to adopting new technologies in assessment;
  • Theoretical frameworks and models for technology-enhanced assessment;
  • Ethical considerations and data privacy in educational technologies.

Dr. Laura Screpanti
Prof. Dr. Lorella Giannandrea
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a double-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Education Sciences is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • technology-enhanced pedagogy
  • personalized feedback
  • assessment as learning
  • automated assessment
  • intelligent tutoring systems
  • artificial intelligence in education
  • learning analytics
  • cyber physical human systems
  • transformative and adaptive feedback

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (5 papers)

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Research

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16 pages, 701 KB  
Article
Formative Feedback Beyond Predefined Options: Eliciting Student-Generated Concepts in Large Classes by Adding Very Short Answers to an Audience Response System
by Aliya Tuktarova, Matthäus Christoph Grasl and Ivo Volf
Educ. Sci. 2026, 16(9), 1415; https://doi.org/10.3390/educsci16091415 - 1 Sep 2026
Viewed by 232
Abstract
Within appropriate instructional frameworks, Audience Response Systems (ARS) can support active learning, retrieval practice, and feedback. However, they typically rely on selecting from predefined options, which limits active answer generation and insight into students’ reasoning. In this feasibility study, we integrated very short [...] Read more.
Within appropriate instructional frameworks, Audience Response Systems (ARS) can support active learning, retrieval practice, and feedback. However, they typically rely on selecting from predefined options, which limits active answer generation and insight into students’ reasoning. In this feasibility study, we integrated very short answers (VSA) into a hybrid ARS and examined whether free-text responses can be reliably aggregated in large classes, whether they extend student concepts beyond predefined options, and how students perceive the approach. First-semester medical students (n = 326) answered 26 questions in alternating multiple-choice (MC) and VSA formats, yielding 11,561 analyzed responses. Responses were automatically aggregated, and the ten largest aggregates per question were displayed in real time to guide discussion and feedback, capturing 74.6% of all responses and 40.3% of VSA responses. Students submitted more responses per answered question in MC than in VSA (mean difference = 0.35, 95% CI [0.31, 0.38], p < 0.001). Post hoc consolidation of conceptually identical expressions yielded 7.6 distinct concepts per question within the Top10 Aggregates, with VSA responses contributing concepts beyond the predefined MC options. Respondents perceived the approach as enhancing understanding and reducing anxiety. By eliciting student-generated concepts and reasoning, including unanticipated ones, VSA integration broadens formative feedback in ARS. Full article
(This article belongs to the Special Issue Trends in Teaching and Learning Technologies for Enhancing Assessment and Feedback)
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21 pages, 684 KB  
Article
Benefits of Technology-Enhanced Assessment for Learning in a Flexible-Paced High School
by Barbara Brown, Nadia Delanoy, Sharon Friesen, Kim Koh and Bruna Nogueira
Educ. Sci. 2026, 16(8), 1277; https://doi.org/10.3390/educsci16081277 - 10 Aug 2026
Viewed by 397
Abstract
There is limited research examining how technology-enhanced assessment functions in nontraditional schools. The following research question guided our study: How are technology-enhanced formative assessment strategies perceived to support student learning in a flexible-paced high school environment? The study employed an educational design approach [...] Read more.
There is limited research examining how technology-enhanced assessment functions in nontraditional schools. The following research question guided our study: How are technology-enhanced formative assessment strategies perceived to support student learning in a flexible-paced high school environment? The study employed an educational design approach to co-develop and implement formative assessment strategies with teacher-participants during six professional learning sessions throughout the school year. Drawing on data from pre- and post-surveys administered to teachers (n = 36), researcher field notes with reflections during the professional learning sessions, and open-ended survey responses from students at the end of the school year (n = 43), we found: (a) a greater reported use of digital methods for formative assessment and a significant increase in the use of authentic, real world assessment criteria; (b) teacher and student perceptions that timely, constructive, and accessible digital feedback supported learning experiences; and (c) recognition that students could be more actively involved in determining assessment criteria. Students also reported benefits from receiving digital feedback, opportunities for self-assessment, and greater clarity in their learning progression. While technology can enrich feedback and efficiency, careful attention must be paid to sustaining equity and learner voice in assessment design. Full article
(This article belongs to the Special Issue Trends in Teaching and Learning Technologies for Enhancing Assessment and Feedback)
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28 pages, 319 KB  
Article
Teachers’ E-Assessment Competences and Practices in the Context of the Digitalization of Secondary Education
by Roumiana Peytcheva-Forsyth, Vasia Delibaltova and Bistra Mizova
Educ. Sci. 2026, 16(3), 397; https://doi.org/10.3390/educsci16030397 - 5 Mar 2026
Viewed by 1290
Abstract
E-assessment is a key component of contemporary teaching and learning. This study examines teachers’ e-assessment competences and practices in Bulgarian secondary education. It applies an embedded mixed-methods design combining quantitative self-assessment data from adapted Bulgarian versions of SELFIE for Teachers and SELFIE for [...] Read more.
E-assessment is a key component of contemporary teaching and learning. This study examines teachers’ e-assessment competences and practices in Bulgarian secondary education. It applies an embedded mixed-methods design combining quantitative self-assessment data from adapted Bulgarian versions of SELFIE for Teachers and SELFIE for Schools tools together with qualitative evidence from semi-structured interviews. Using a nationally representative selection of 30 schools, quantitative data was collected from 574 teachers related to their e-assessment competences and from 655 teachers on institutional e-assessment practices, which was complemented by interviews with 220 teachers. Teachers’ self-assessed competences were found to be largely at the lower levels of awareness and limited implementation, rather than systematic use, with few teachers reporting advanced competence. Although institutional support for e-assessment is perceived as strong, this is not reflected in pedagogic practice. The interview data shows teachers’ conceptual ambiguities, with formative e-assessment implemented in fragmented ways and rarely explicitly recognised. Innovative approaches, such as peer assessment, self-assessment, and the systematic use of digital evidence to inform instruction, are rarely used. The study reveals a misalignment between teachers’ perceived competences, institutional conditions, and actual practice, highlighting the need for targeted professional development and clearer conceptual framing to support the meaningful integration of e-assessment. Full article
(This article belongs to the Special Issue Trends in Teaching and Learning Technologies for Enhancing Assessment and Feedback)
26 pages, 1815 KB  
Article
AI-Generated Dialogic Feedback: Designing a Pedagogical Chatbot Grounded in Literacy Resilience Principles
by Alisa Amir
Educ. Sci. 2026, 16(2), 318; https://doi.org/10.3390/educsci16020318 - 16 Feb 2026
Cited by 3 | Viewed by 2131
Abstract
Artificial intelligence (AI) has reshaped contemporary approaches to teaching, assessment, and feedback. Most AI systems provide reactive feedback, offering instant answers that reduce learners’ cognitive engagement and sense of agency. In contrast, Mili was developed as a proactive pedagogical intelligence that asks guiding [...] Read more.
Artificial intelligence (AI) has reshaped contemporary approaches to teaching, assessment, and feedback. Most AI systems provide reactive feedback, offering instant answers that reduce learners’ cognitive engagement and sense of agency. In contrast, Mili was developed as a proactive pedagogical intelligence that asks guiding questions and encourages learners to construct their own responses. Through this design, feedback becomes a process of learning rather than an evaluative mechanism. Mili is a Hebrew-language educational chatbot grounded in principles of dialogic feedback, pedagogical mediation, and literacy resilience. Its goal is to create a metacognitive literacy dialogue in which questions replace answers and learning becomes an act of reflection and self-inquiry. The development followed a Design-Based Research approach involving iterative cycles of design, training, and testing. At each stage, pedagogical prompts were crafted to simulate authentic teacher–learner dialogue, including clarifying questions, pedagogical delay, and emotional reinforcement. This process enabled an exploration of how AI can mediate feedback that stimulates deeper cognitive engagement. The resulting model demonstrates proactive dialogic feedback in which AI does not simply respond but initiates reflective dialogue. Simulated interactions with Mili reveal how such feedback supports the three dimensions of literacy resilience: linguistic-cognitive, metacognitive, and emotional. Mili represents a conceptual shift in AI-based feedback, moving from response to process, from outcome to mediation, and from reactive AI to learning-generative AI. The study makes a theoretical contribution by articulating a model of pedagogically mediated AI and a practical contribution by developing a feedback tool that fosters inquiry, reflection, and literacy resilience in learners and teachers. Full article
(This article belongs to the Special Issue Trends in Teaching and Learning Technologies for Enhancing Assessment and Feedback)
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Other

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40 pages, 554 KB  
Systematic Review
The Impact of AI-Driven Formative Feedback in Gamified Learning Environments: A Systematic Literature Review
by Rommel Gutierrez, Jaime Govea and Estefania Valencia-Silva
Educ. Sci. 2026, 16(6), 858; https://doi.org/10.3390/educsci16060858 - 29 May 2026
Viewed by 918
Abstract
Artificial intelligence (AI) and gamification have independently demonstrated potential for enhancing learning outcomes, yet how these two approaches converge when AI technologies are used to deliver formative feedback within gamified learning environments remains insufficiently synthesised. This systematic review examines the AI technologies employed, [...] Read more.
Artificial intelligence (AI) and gamification have independently demonstrated potential for enhancing learning outcomes, yet how these two approaches converge when AI technologies are used to deliver formative feedback within gamified learning environments remains insufficiently synthesised. This systematic review examines the AI technologies employed, their reported effects on learning outcomes, the gamification configurations combined with AI feedback, and the ethical considerations identified across this emerging intersection. We reviewed 32 peer-reviewed empirical studies published from 2020 to 2025, identified from Scopus, Web of Science, IEEE Xplore, and ERIC following PRISMA guidelines. Findings show that adaptive scaffolding emerges as the dominant pedagogical function of AI in formative feedback design, accounting for nearly half of the corpus and aligning the field’s empirical centre with its theoretical centre. The reviewed studies converge in reporting positive effects across cognitive, affective, and metacognitive outcome domains, with twenty-one studies reporting statistically significant positive effects and no study reporting overall negative effects. AI-driven personalisation of gamification accounts for the largest integration pattern and concentrates the controlled comparisons that isolate the AI contribution. The corpus demonstrates substantive engagement with established ethical practices, while emerging considerations regarding algorithmic bias, AI hallucinations, and digital equity admit concrete design responses operationalised through a minimum reporting standard proposed in this review. Full article
(This article belongs to the Special Issue Trends in Teaching and Learning Technologies for Enhancing Assessment and Feedback)
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