We present an annotation approach to capturing emotional and cognitive empathy in student-written peer reviews on business models in German. We propose an annotation scheme that allows us to model emotional and cognitive empathy scores based on three types of review components. Also, we conducted an annotation study with three annotators based on 92 student essays to evaluate our annotation scheme. The obtained inter-rater agreement of  =0.79 for the components and the multi =0.41 for the empathy scores indicate that the proposed annotation scheme successfully guides annotators to a substantial to moderate agreement. Moreover, we trained predictive models to detect the annotated empathy structures and embedded them in an adaptive writing support system for students to receive individual empathy feedback independent of an instructor, time, and location. We evaluated our tool in a peer learning exercise with 58 students and found promising results for perceived empathy skill learning, perceived feedback accuracy, and intention to use. Finally, we present our freely available corpus of 500 empathy-annotated, student-written peer reviews on business models and our annotation guidelines to encourage future research on the design and development of empathy support systems. Empathy is an elementary skill in society for daily interaction and professional communication and is therefore elementary for educational curricula (e.g., Learning Framework 2030 (OECD, 2018)). It is the ability to simply understand the other person's perspective [ . . . ] and to react to the ob-Figure 1: Empathy annotation scheme. First, a text paragraph is classified into a peer review component ( strengths, weakness, improvement suggestions ). Second, the same annotator is then scoring the cognitive and emotional empathy level of the components based on our annotation guideline on a 1-to-5 scale. served experiences of another, (Davis, 1983, p.1) 1 . Empathy skills not only pave the foundation for successful interactions in digital companies, e.g., in agile work environments (Luca and Tarricone, 2001), but they are also one of the key abilities in the future that will distinguish the human workforce and artificial intelligence agents from one another (Poser and Bittner, 2020). However, besides the growing importance of empathy, research has shown that empathy skills of US college students decreased from 1979 to 2009 by more than thirty percent and even more rapidly between 2000 to 2009 (Konrath et al., 2011). On these grounds, the Organization for Economic Cooperation and Development (OECD) claims that the training for empathy skills should receive a more prominent role in today's higher education (OECD, 2018). 1 Being aware that empathy is a multidimensional construct, in this study, we focus on emotional and cognitive empathy (Spreng et al., 2009; Davis, 1983). To train students with regard to empathy, educational institutions traditionally rely on experiential learning scenarios, such as shadowing, communication skills training, or role playing (Lok and Foster, 2019; van Berkhout and Malouff, 2016). 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 Timperley, 2007). One possible path for providing individual learning conditions is to leverage recent developments in computational linguistics. Language-based models enable the development of writing support systems that provide tailored feedback and recommendations (Santos et al., 2018), e.g., like those already used for argumentation skill learning (Wambsganss et al., 2020a, 2021b). Recently, studies have started investigating elaborated models of human emotions (e.g., Wang et al. (2016), Abdul-Mageed and Ungar (2017), Buechel and Hahn (2018), or Sharma et al. (2020)), but available corpora for empathy detection are still rare. Only a few studies address the detection and prediction of empathy in natural texts (Khanpour et al., 2017; Xiao et al., 2012), and, to the best of our knowledge, only one corpus is publicly available for empathy modelling based on news story reactions (Buechel et al., 2018). Past literature therefore lacks 1) publicly available empathy annotated data sets, 2) empathy annotation models based on rigorous annotation guidelines combined with annotation studies to assess the quality of the data, 3) the alignment of empathy in literature on psychological constructs and theories, and 4) an embedding and real-world evaluation of novel modelling approaches in collaborative learning scenarios (Rose et al., 2008). We introduce an empathy annotation scheme and a corpus of 500 student-written reviews that are annotated for the three types of review components, strengths, weaknesses , and suggestions for improvements , and their embedded emotional and cognitive empathy level based on psychological theory (Davis, 1983; Spreng et al., 2009). We trained different models and embedded them as feedback algorithms in a novel writing support tool, which provided students with individual empathy feedback and recommendations in peer learning scenarios. The measured empathy skill learning (Spreng et al., 2009), the perceived feedback accuracy (Pod-sakoff and Farh, 1989), and the intention to use (Venkatesh and Bala, 2008) in a controlled evaluation with 58 students provided promising results for using our approach in different peer learning scenarios to offer quality education independent of an instructor, time, and location. Our contribution is fourfold: 1) we derive a novel annotation scheme for empathy modeling based on psychological theory and previous work on empathy annotation (Buechel et al., 2018); 2) we present an annotation study based on 92 student peer reviews and three annotators to show that the annotation of empathy in student peer reviews is reliably possible; 3) to the best of our knowledge, we present the second freely available corpus for empathy detection in general and the first corpus for empathy detection in the educational domain based on 500 student peer reviews collected in our lecture about business innovation in German; 4) we embedded our annotation approach as predictive models in a writing support system and evaluated it with 58 students in a controlled peer learning scenario. We hope to encourage research on student-written empathetic texts and writing support systems to train students' empathy skills based on NLP towards a quality education independent of a student's location or instructors. The Construct of Empathy The ability to perceive the feelings of another person and react to their emotions in the right way requires empathy  the ability  of one individual to react to the observed experiences of another (Davis (1983), p.1). Empathy plays an essential role in daily life in many practical situations, such as client communication, leadership, or agile teamwork. Despite the interdisciplinary research interest, the term empathy is defined from multiple perspectives in terms of its dimensions or components (Decety and Jackson, 2004). Aware of the multiple perspectives on empathy, in this annotation study, we focused on the cognitive and emotional components of empathy as defined by Davis (1983) and Lawrence et al. (2004). Therefore, we follow the Toronto Empathy Scale' (Spreng et al., 2009) as a synthesis of instruments for measuring and validating empathy. Hence, empathy consists of both emotional and cognitive components (Spreng et al., 2009). While emotional empathy lets us perceive what other people feel, cognitive empathy is the human ability to recognize and understand other individuals (Lawrence et al., 2004). Emotion and Empathy Detection In NLP, the detection of empathy in texts is usually regarded as a subset of emotion detection, which in turn is often referred to as part of sentiment analysis. The detection of emotions in texts has made major progress, with sentiment analysis being one of the most prominent areas in recent years (Liu, 2015). However, most scientific studies have been focusing on the prediction of the polarity of words for assessing negative and positive notions (e.g., in online forums (Abbasi et al., 2008) or twitter postings (Rosenthal et al., 2018)). Moreover, researchers have also started investigating more elaborated models of human emotions (e.g., Wang et al. (2016), Abdul-Mageed and Ungar (2017), and Mohammad and Bravo-Marquez (2017)). Several corpora exist where researchers have annotated and assessed the emotional level of texts. For example, Scherer and Wallbott (1994) published an emotion-labelled corpus based on seven different emotional states. Strapparava and Mihalcea (2007) classified news headlines based on the basic emotions scale of Ekman (1992) (i.e., anger, disgust, fear, happiness, sadness and surprise ). More recently, Chen et al. (2018) published EmotionLines , an emotion corpus of multi-party conversations, as the first data set with emotion labels for all utterances was only based on their textual content. Bostan and Klinger (2018) presented a novel unified domain-independent corpus based on eleven emotions as the common label set. However, besides the multiple corpora available for emotion detection in texts, corpora for empathy detection are rather rare. As Buechel et al. (2018) also outline, the construction of corpora for empathy detection and empathy modelling might be less investigated due to various psychological perspectives on the construct of empathy. Most of the works for empathy detection focus, therefore, on spoken dialogue, addressing conversational agents, psychological interventions, or call center applications (e.g., McQuiggan and Lester (2007), Perez-Rosas et al. (2017), Alam et al. (2018), Sharma et al. (2020)) rather than written texts. Consequently, there are hardly any corpora available in different domains and languages that enable researchers in training models to detect the empathy level in texts, e.g., by providing students with individual empathy feedback (Buechel et al., 2018). Empathy Annotated Corpora and Annotation Schemes Only a few studies address the detection and prediction of empathy in natural language texts (e.g., Khanpour et al. (2017) and Xiao et al. (2012)). Presenting the first and only available gold standard data set for empathy detection, Buechel et al. (2018) constructed a corpus in which crowd-workers were asked to write emphatic reactions to news stories. Before the writing tasks, the crowd-workers were asked to conduct a short survey with self-reported items to measure their empathy level and their personal distress based on Batson et al. (1987). The scores from the survey were then taken as the annotation score for the overall news reaction message. The final corpus consisted of 1,860 annotated messages (Buechel et al., 2018). Nevertheless, previous empathy annotations on natural texts merely focused on intuition-based labels instead of rigorous annotation guidelines combined with annotation studies by researchers to assess the quality of the corpora (i.e., as is done for corpora of other writing support tasks, e.g., argumentative student essays by Stab and Gurevych (2017)). Moreover, previous annotations have mostly been conducted at the overall document level, resulting in one generic score for the whole document, which makes the corpus harder to apply to writing support systems. Consequently, there is a lack of linguistic corpora for empathy detection in general and, more specifically, for training models that provide students with adaptive support and feedback about their empathy in common pedagogical scenarios like large-scale lectures or the growing field of MOOCs (Wambsganss et al., 2021c, 2020b). In fact, in the literature about computer-supported collaborative learning (Dillenbourg et al., 2009), we found only one approach by Santos et al. (2018) that used a dictionary-based approach to provide students with feedback on the empathy level of their texts. We aim to address this literature gap by presenting and evaluating an annotation scheme and an annotated empathy corpus built on student-written texts with the objective to develop intelligent and accurate empathy writing support systems for students. with feedback on previously developed business models. Peer reviews are a modern learning scenario in large-scale lectures, enabling students to reflect on their content, receive individual feedback from peers, and thus deepen their understanding of the content (Rietsche and Sollner, 2019). Moreover, they are easy to set up in traditional large-scale learning scenarios or the growing field of distance-learning scenarios such as MOOCs. This can be leveraged to train skills such as the ability to appropriately react to other students' perspectives (e.g., Santos et al. (2018)). Therefore, we aim to create an annotated corpus to provide empathy feedback based on a data set that A) is based on real-world student peer reviews, B) consists of a sufficient corpus size to be able to train models in a real-world scenario and C) follows a novel annotation guideline for guiding the annotators towards an adequate agreement. Hence, we propose a new annotation scheme to model peer review components and their emotional and cognitive empathy levels that reflect the feedback discourse in peer review texts. We base our empathy annotation scheme on emotional and cognitive empathy following Davis (1983) and Spreng et al. (2009) guided by the study of Buechel et al. (2018). To build a reliable corpus, we followed a 4-step methodology: 1) we examined scientific literature and theory on the construct of empathy and on how to model empathy structures in texts from different domains; 2) we randomly sampled 92 student-generated peer reviews and, on the basis of our findings from literature and theory, developed a set of annotation guidelines consisting of rules and limitations on how to annotate emphatic review discourse structures; 3) we applied, evaluated, and improved our guidelines with three native speakers of German in a total of eight consecutive workshops to resolve annotation ambiguities; 4) we followed the final annotation scheme based on our 14-page guidelines to annotate a corpus of 500 student-generated peer reviews. 2 3.1 Data Source We gathered a corpus of 500 student-generated peer reviews written in German. The data was collected in a business innovation lecture in a master's program at a Western European university. In this lecture, around 200 students develop and present a 2 The annotation guidelines as well as the entire corpus can be accessed at https://github.com/thiemowa/ empathy_annotated_peer_reviews . new business model for which they receive three peer reviews each. Here, a fellow student from the same course elaborates on the strengths and weaknesses of the business model and gives recommendations on what could be improved. We collected a random subset of 500 of these reviews from around 7,000 documents collected from the years 2014 to 2018 in line with the ethical guidelines of our university and with approval from the students to utilize the writings for scientific purposes. An average peer review consists of 200 to 300 tokens (in our corpus we counted a mean of 19 sentences and 254 tokens per document). A peer review example is displayed in Figure 2. 3.2 Annotation Scheme Our objective is to model the empathy structures of student-generated peer reviews by annotating the review components and their emotional and cognitive empathy levels. Most of the peer reviews in our corpus followed a similar structure. They described several strengths or weaknesses of the business model under consideration, backing them up by examples or further elaboration. Moreover, the students formulated certain suggestions for improvements of the business model. These review components (i.e., strengths, weaknesses, and suggestions for improvement ) were written with different empathetic levels, sometimes directly criticizing the content harshly, sometimes empathetically referring to weaknesses as further potentials for improvement with examples and explanation. We aim to capture these empathic differences between the peer reviews with two empathy level scores, the cognitive empathy level of a certain review component and the emotional empathy level of a certain component. Our basic annotation scheme is illustrated in Figure 1. 3.2.1 Review Components For the review components, we follow established models of feedback structures suggested by feedback theory (e.g., Hattie and Timperley (2007) or Black and Wiliam (2009)). A typical peer review, therefore, consists of three parts: 1) elaboration of strengths, 2) elaboration of weaknesses, and 3) suggestions for improvements (to answer Where am I going and how am I going? and Where do I go next? , i.e., Hattie and Timperley (2007)). Accordingly, the content of a review consists of multiple components, including several controversial statements (e.g., a claim about a strength or weakness of a business model) that are usually supported by elaborations or examples (i.e., a premise ) (Toulmin, 1984). Also, in the domain of student-written peer reviews, we found that a standpoint and its elaboration are the central element of a review component. Accordingly, we summarized all the claims and premises which described positive aspects of a business model as strengths . All content (claims and premises) describing negative aspects were modelled as weaknesses , while claims and premises with certain content for improvement were modelled as suggestions for improvement , following the structure of a typical review. Besides the content, syntactical elements and key words were used as characteristics for the compound classification, e.g., most students introduced a review component by starting with structural indications such as Strengths: or Weaknesses: in their peer review texts. 3.2.2 Empathy Level To capture the differences in the empathy levels of the peer reviews (i.e., the way the writer was conveying their feedback (Hattie and Timperley, 2007)), we followed the approach of Davis (1983) and Spreng et al. (2009) for cognitive and emotional empathy. Cognitive empathy (perspective taking) is the writer's ability to use cognitive processes, such as role taking, perspective taking, or  decentering , while evaluating the peers' submitted tasks. The student sets aside their own perspective and steps into the shoes of the other. Cognitive empathy can happen purely cognitively, in that there is no reference to any affective state, (Baron-Cohen and Wheelwright, 2004) but it mostly includes understanding the other's emotional state as well. The following example displays high cognitive empathy: You could then say, for example, Since market services are not differentiated according to customer segments and locations, the following business areas result... And that due to the given scope of this task you will focus on the Concierge-Service business segment.' After that, you have correctly only dealt with this business seg-ment. Emotional empathy (emphatic concern) is the writer's emotional response to the peers' affective state. The students can either show the same emotions as read in the review or simply state an appropriate feeling towards the peer. Typical examples include sharing excitement with the peer about the business model submitted or showing concern over the peer's opinion. The following example depicts high emotional empathy: I think your idea is brilliant! . Both constructs are measured on a scale from 1-5 following the empathy scale range of Moyers and Martin (2010), with every level being precisely defined in our annotation guidelines. A summary of the definitions for both empathy level scores are displayed in Table 1 and Table 2. A more detailed description of both scores can be found in the appendix in Table 7 and Table 8. 3 Figure 2 illustrates an example of an entire peer review that is annotated for strength, weakness and suggestion for improvement and the cognitive and emotional empathy scores. 4 Figure 2: Fully annotated example of a peer review. Three native German speakers annotated the peer reviews independently from each other for the components strengths, weaknesses and suggestions for improvement , as well as their cognitive and emotional empathy levels according to the annotation guidelines we specified. The annotators were master's students in business innovation from a European university with bachelor's degrees in business administration and were, therefore, domain experts in the field of business models. Inspired by Stab 3 More elaborated definitions, examples, and key word lists for both empathy scales can be found in our annotation guidelines. and Gurevych (2017), our guidelines consisted of 14 pages, including definitions and rules for how the review components should be composed, which annotation scheme was to be used, and how the cognitive and emotional empathy level were to be judged. Several individual training sessions and eight team workshops were performed to resolve disagreements among the annotators and to reach a common understanding of the annotation guidelines on the cognitive and emotional empathy structures. We used the tagtog annotation tool, 5 which offers an environment for cloud-based annotation in a team. First, a text was classified into peer review components ( strengths, weaknesses, suggestions for improvement , or none ) by the trained annotators. Second, the same annotator then scored the cognitive and emotional empathy levels of each component based on our annotation guideline on a one to five scale. After the first 92 reviews were 5 https://tagtog.net/ annotated by all three annotators, we calculated the inter-annotator agreement (IAA) scores (see Section 4.1). 6 As we obtained satisfying results, we proceeded with two annotators annotating 130 remaining documents each and the senior annotator annotating 148 peer reviews, resulting in 408 additional annotated documents. Together with the 92 annotations of the annotation study of the senior annotator (the annotator with the most reviewing experience), we counted 500 annotated documents in our final corpus. To evaluate the reliability of the review components and empathy level annotations, we followed the approach of Stab and Gurevych (2014). Review Components Concerning the review components, two strategies were used. Since there were no predefined markables, annotators not only had to identify the type of review component but also its boundaries . In order to assess the latter, we use Krippendorff's  U (Krippendorff, 2004), which allows for an assessment of the reliability of an annotated corpus, considering the differences in the markable boundaries. To evaluate the an-notators' agreement in terms of the selected category of a review component for a given sentence, we calculated the percentage agreement and two chance-corrected measures, multi (Fleiss, 1971) and Krippendorff's  (Krippendorff, 1980). Since each annotation always covered a full sentence (or a sequence of sentences), we operated at the sentence level for calculating the reliability of the annotations in terms of the IAA. Table 3 displays the resulting IAA scores. The obtained scores for Krippendorff's  indicated an almost perfect agreement for the strengths components and a substantial agreement for both the weaknesses and the suggestions for improvement components. The unitized  of strengths, weaknesses and suggestions for improvement annotations was slightly smaller compared to the sentence-level agreement. Thus, the boundaries of review components were less precisely identified in comparison to the classification into review components. Yet the scores still suggest that there was a moderate level of agreement between the annotators for the strengths and a fair agreement for the weaknesses and the suggestions for improvement. With a score of  U =90.32%, the boundaries of the non-annotated text units were more reliably detected, indicating an almost perfect agreement between the annotators. Percentage agreement, multi , and Krippendorff's  were considerably higher for the non-annotated spans as compared to the strengths, weaknesses, and suggestions for improvement, indicating an almost perfect agreement between the annotators. Hence, we conclude that the annotation of the review components in student-written peer reviews is reliably possible . Empathy Level To assess the reliability of the cognitive and emotional empathy level annotations, we calculated the multi for both scales. For the cognitive empathy level, we received a multi of 0.41 for both the emotional and cognitive empathy level, suggesting a moderate agreement between the annotators in both cases. Thus, we conclude that the empathy level can also be reliably annotated in student-generated peer reviews. To analyze the disagreement between the three annotators, we created a confusion probability matrix (CPM) (Cinkova et al., 2012) for the review components and the empathy level scores. The results can be found in Section C of the appendix. The corpus we compiled consists of 500 student-written peer reviews in German that were composed of 9,614 sentences with 126,887 tokens in total. Hence, on average, each document had 19 sentences and 254 tokens. A total of 2,107 strengths, 3,505 weaknesses and 2,140 suggestions for improvement were annotated. Tables 4, 5, and 6 present some detailed statistics on the final corpus. Moreover, Figure 3 displays the distribution of the empathy scores in the annotated dataset. Both the cognitive and the emotional empathy levels approximately follow a normal distribution with a mean score of 2.94 and 3.22, respectively (see Table 6). We measured only a low correlation of 0.38 between the scores of cognitive and emotional empathy. Modelling Cognitive and Emotional Empathy The empathy detection task is considered a paragraph-based, multi-class classification task, where each paragraph is either considered to be a strength, weakness , or a suggestion for improvement and has a non-empathic, neutral, or em-pathic cognitive and emotional empathy level. Therefore, we assigned the levels of our cognitive and emotional empathy scores to three different labels: level 1 and 2 were assigned to a non-empathic text label, level 3 to a neutral label, and levels 4 and 5 to anempathic label . We split the data into 70% training, 20% validation, and 10% test data. To apply the model, the corpus texts were split into word tokens. The model performances were measured in terms of accuracy, precision, recall, and f1-score. We trained a predictive model following the architecture of Bidirectional Encoder Representations from Transformers (BERT) proposed by Devlin et al. (2018). We used the BERT model from deepset , 7 since it is available in German and provides a deep pretrained model that was unsupervised while training on domain-agnostic German corpora (e.g., the German Wikipedia). The best performing paramenter combination for our BERT model incorporated a dropout probability of 10% and a learning rate of 3e -5 , and the number of epochs were 3. After several iterations, we reached a micro f1-score of 74.96% for the detection of the emotional empathy level and 69.98% for the detection of the cognitive empathy level of a text paragraph. Moreover, we reached an f1-score of 94.83% to predict a text paragraph as a strength, a 64.28% to predict a text paragraph as a weakness, 7 https://github.com/deepset-ai/FARM and 59.79% to predict suggestions for improvement. To ensure the validity of our BERT model, we benchmarked against bidirectional Long-Short-Term-Memory-Conditional-Random-Fields classi-fiers (BiLSTM-CRF). In combination with the corresponding embeddings vocabulary (GloVe) (Pen-nington et al., 2014), our LSTM reached an unsatisfying f1-score of 61% for detecting the emotional empathy level and 51% for detecting the cognitive empathy level. Evaluation in a Peer Learning Setting We designed and built an adaptive writing support system that provides students with individual feedback on their cognitive and emotional empathy skills. The application is illustrated in Figure 4. We embedded our system into a peer writing exercise where students were asked to write a peer review on a business model. During this writing task, they received adaptive feedback on the cognitive and emotional empathy level based on our model. The evaluation was conducted as a web experiment facilitated by the behavioral lab of our university, and thus, designed and reviewed according to the ethical guidelines of the lab and the university. We received 58 valid results (mean age = 23.89, SD= 3.07, 30 were male, 28 female). The participants were told to read an essay about a business model of a peer student. Afterwards, they were asked to write a business model review for the peer by providing feedback on the strengths, weaknesses, and suggestions for improvement of the particular business model. After the treatment, we measured the intention to use (ITU) (Venkatesh and Bala, 2008) by asking three items. We also asked the participants to judge their perceived empathy skill learning (PESL) by asking two items that covered cognitive and emotional empathy skills (Spreng Figure 4: Screenshot of a trained model on our corpus as an adaptive writing support system. et al., 2009; Davis, 1983). Finally, we surveyed the perceived feedback accuracy (PFA) (Podsakoff and Farh, 1989) to control the accuracy of our model. All constructs were measured with a 1-to-7 point Likert scale (1: totally disagree to 7: totally agree, with 4 being a neutral statement). 8 Furthermore, we asked three qualitative questions:  What did you particularly like about the use of the tool? ,  What else could be improved? , and  Do you have any other ideas?  and captured the demographics. In total, we asked 13 questions. All participants were compensated with an equivalent of about 12 USD for a 25 to 30 minute experiment. Results Participants judged their empathy skill learning with a mean of 5.03 (SD= 1.05). Concerning the PFA, the subjects rated the construct with a mean of 4.93 (SD= 0.94). The mean value of intention to use of the participants using our application as a writing support tool in peer learning scenarios was 5.14 (SD= 1.14). The mean values of all three constructs were very promising when comparing the results to the midpoints. All results were better than the neutral value of 4, indicating a positive evaluation of our application for peer learning tasks. We also asked open questions in our survey to receive the participants' opinions about the tool they used. The general attitude was very positive. Participants positively mentioned the simple and easy interaction, the distinction between cognitive and emotional empathy feedback, and the overall empathy score together with the adaptive feedback message several times. However, participants also said that the tool should provide even more detailed feedback based on more categories and should pro-8 The exact items are listed in the appendix. vide concrete text examples on how to improve their empathy score. We translated the responses from German and clustered the most representative responses in Table 16 in the appendix. We introduce a novel empathy annotation scheme and an annotated corpus of student-written peer reviews extracted from a real-world learning scenario. Our corpus consisted of 500 student-written peer reviews that were annotated for review components and their emotional and cognitive empathy levels. Our contribution is threefold: 1) we derived a novel annotation scheme for empathy modeling based on psychological theory and previous work for empathy modeling (Buechel et al., 2018); 2) we present an annotation study based on 92 student peer reviews and three annotators to show that the annotation of empathy in student peer reviews is reliably possible ; and 3) to the best of our knowledge, we present the second freely available corpus for empathy detection and the first corpus for empathy detection in the educational domain based on 500 student peer reviews in German. For future research, this corpus could be leveraged to support students' learning processes, e.g., through a conversational interaction (Zierau et al., 2020). However, we would also encourage research on the ethical considerations of empathy detection models in user-based research (i.e., Wambsganss et al. (2021a)). We, therefore, hope to encourage future research on student-generated empathetic texts and on writing support systems to train empathy skills of students based on NLP towards quality education independent of a student's location or instructors.
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