Looking for Answers: A Scoping Review of Academic Help-Seeking in Digital Higher Education Research

Abstract

As Higher Education (HE) institutions expand their digital resources, students may be struggling to engage effectively with these tools. Academic help-seeking (AHS) is a useful framework for exploring help-seeking behaviour and could be applied to HE contexts, yet its application has been reported as inconsistent. Given the possible utility of this concept to support the growth in technology, a review of how academic help-seeking theory is applied in digital contexts can help to consolidate current understanding and guide future research. This scoping review examines the intersection of AHS and digital technology use in HE through analysis of peer-reviewed literature (2019–2024). Several gaps emerge: bias towards human-centred support, limited investigation of help-avoidance behaviours, insufficient attention to early help-seeking stages, and few studies examining spontaneous help-seeking in authentic digital learning environments. These findings indicate the need for expanded theoretical frameworks that better reflect modern learning behaviours and environments, alongside more diverse research approaches to understand how students integrate both human and non-human help sources in contemporary HE contexts.

1. Introduction

The rapid and continuous digital transformation of higher education creates two interconnected challenges: persistent digital inequalities that affect how well students navigate these learning environments (Van Dijk, 2020), and a research knowledge gap that limits our ability to develop an effective response. A potential clarifying source for both challenges is the crossover between digital and help-seeking behaviours. While individual studies explore various digital help-seeking aspects, no recent review has mapped what is collectively known about this area. Previously identified knowledge gaps in this area include the following. The field’s focus on human-to-human interactions has left digital help sources underexplored (Giblin et al., 2021; Puustinen & Rouet, 2009). Methodologically, the predominance of quantitative approaches may inadequately capture the complexity of help-seeking behaviours, with researchers calling for more qualitative and naturalistic methodologies to understand authentic student experiences (Evenhouse et al., 2020; Maclaren, 2017). Despite early theoretical recognition that AI tools would require reconceptualizing help-seeking beyond social interactions (Keefer & Karabenick, 1998), research has not kept pace with technological developments.

Our approach to these challenges recognises that effective support systems require robust theoretical foundations. While theoretical development may not yield immediate practical interventions, it provides the conceptual tools necessary for understanding and addressing complex help-seeking behaviours. This is particularly crucial for capturing help-avoidance alongside help-seeking behaviours, as understanding why students do not seek available help may be as important as understanding how they do seek help when designing support systems. Given these theoretical and methodological challenges, a systematic review is needed to map how contemporary research addresses digital help-seeking behaviours, examine the theoretical frameworks being employed, and identify what is known about students’ navigation of both human and digital help sources.

Research Questions

For this paper the authors will adopt pragmatic interpretivist perspectives, accepting that there are multiple truths and that these truths change based on context (Cohen et al., 2018), whilst also accepting the value of Dewey’s “warranted assertions” (Biesta, 2010). This approach recognises that while conclusions may be specific to a particular situation, they can still offer valuable insights to others in other contexts, allowing for a carefully considered use of relevant findings from different contexts and situations.

The research questions of this scoping review will follow a pragmatic research design, exploring practical and theoretical concerns. The practical dimension will be explored by identifying contemporary sources of help when undertaking digital tasks or working within digitally supported HE environments. Suggestions for using and conceptualising AHS frameworks in future research more effectively will also be proffered.

With regards to the theoretical concern, the authors will apply a critical lens to the captured AHS literature, investigating whether calls for an integrative framework of AHS combining both the social and informational sources of help have been embraced. Qualitative, quantitative and mixed methods studies will be included and the influence conceptions of AHS have on the body of research will be discussed.

To guide the review the following questions are therefore posed:

• Q1. What is the nature of contemporary HE AHS literature containing a digital aspect?
• Q2. To what extent are these studies grounded in theory and how are these theoretical and conceptual frameworks described?
• Q3. Which sources (or resources) of help are identified across this literature?

The following section first explores the theoretical foundations of academic help-seeking before providing a definition of the extent of digital technologies within HE. It then outlines the spectrum from human-to-human support to digital help-seeking before concluding by considering persistent digital barriers to student help-seeking. This will highlight the need for this review and illustrate particular elements of the current theoretical landscape that will be used in later analysis.

2. Theoretical Foundations

2.1. Help Seeking

Nelson-Le Gall’s (1981, 1985) early work on help-seeking provides the basis for much of the AHS literature. Carried out over 40 years ago and set within primary education contexts the author outlined five stages in the AHS process: awareness of need for help, decision to seek help, identify potential helpers, employ strategies to elicit help, and react to help-seeking attempts (Nelson-Le Gall, 1985). Following this work, AHS has maintained a strong leaning towards mainly human or social sources of help such as teachers and peers (Giblin et al., 2021; Puustinen & Rouet, 2009). In more recent times, these theories have been applied in HE and have begun to increasingly consider the digital domain. AHS is also increasingly conceptualised as a self-regulatory strategy. Self-Regulated Learning (SRL) is a theoretical framework that describes how learners actively monitor and control their thoughts, behaviours, and motivations (Zimmerman, 2008). AHS is conceived within this framework as something that learners employ when they recognise gaps in their understanding, actively seek assistance to bridge these gaps, and in how they reflect on help-seeking outcomes.

Karabenick and Dembo’s (2011) AHS definition expands upon Nelson-Le Gall’s adaptive help-seeking process model focusing and integrating multiple stages including cognitive (whether there is a problem), affective-emotional (whether help is needed), contextual-emotional (whether to seek help), and social (who to ask and solicit help) which also aligns with broader SRL definitions (Fong et al., 2023). It identifies discrete phases of (1) decide whether a problem exists; (2) decide whether assistance is required or desired; (3) determine whether to request assistance; (4) choose the type of help you want (executive or instrumental); (5) choose the person you want to seek for help; (6) ask for help; (7) get help and (8) process the help you got (Karabenick & Dembo, 2011). An additional reason for this selection is it incorporates a framework by Nelson-Le Gall (1981) for identifying the different help-seeking motivations of executive and instrumental. Executive refers to a motivation to get the immediate problem solved whereas instrumental is a motivation to learn through the process of seeing the problem solved and therefore needing less help the next time.

Although these complementary theories provide valuable conceptual underpinnings for AHS research, questions remain about how consistently and comprehensively they are used across the research.

2.2. Digital Technologies in Higher Education

This review uses the term ‘digital technologies in HE’ to reference the wider HE learning experience beyond classroom learning to include interactions with institutional resources, services, and peers (Ertl et al., 2008). Digital technology plays a crucial role through Virtual Learning Environments (VLEs), library databases, subject-specific software, online support services, and various digital devices (Sharpe & Benfield, 2005; Jahnke, 2023). While students may be familiar with basic digital tools, the HE environment demands more sophisticated engagement and new digital competencies. Furthermore, within this context a wide range of support channels could conceivably be employed as part of AHS, such as, FAQs live tutor chat, discussion forums, GenAI based systems (e.g., chatbots) and tools integrated into VLE’s such as Canvas, Moodle, Blackboard and Google Classroom.

2.3. Human-to-Human and Digital AHS?

This review tracks how researchers define and approach AHS, specifically exploring the breadth of applications beyond human facilitation. It questions whether critiques (Giblin et al., 2021; Puustinen & Rouet, 2009) about an over-emphasis on human sources of help remain valid in HE contexts and explores how researchers conceptualise the increasingly blurred boundaries between human and non-human help resources. Despite the increasing importance of understanding how students seek help in digitally rich environments, there appears to be limited systematic analysis. For example, Broadbent and Howe (2023) note that whilst there is a substantial body of literature on AHS within learning environments there is only a small proportion focused on AHS within online learning environments. Similarly, Sumadyo et al.’s (2021) systematic literature review exploring AHS during collaborative online learning, reported difficulties finding any papers examining both human and non-human AHS.

Notable AHS research studies that have explored the impact of new technologies and attempted to theorise their integration with more direct human sources of help include Keefer and Karabenick (1998). They queried whether sophisticated tools using machine learning will require a reconsideration of the importance of the human aspect of AHS. Puustinen and Rouet (2009) challenged the dichotomy between help-seeking (from human sources) and information searching (from non-human sources), suggesting that modern technology-enhanced learning environments necessitate a more integrated AHS framework. Whilst noting that ‘strictly speaking’ all support systems have a human influence in a designer or author, and in turn human experts are informed by an information system previously shaped by a human. They argue that “help seeking be regarded as a more comprehensive construct than information searching that includes traditional human support, but also those situations in which the student/expert interaction is ‘mediated’ by an information system” (Puustinen & Rouet, 2009, p. 1018). Their work proposes reconceptualising AHS along a continuum based on the helper’s adaptability to learner needs, whether the helper is a textbook, search engine or human expert.

Makara and Karabenick (2013) also outlined a more nuanced framework for understanding AHS in digital learning environments. Their framework identifies four key dimensions of help-seeking: formal versus informal sources, personal versus impersonal relationships, mediated versus face-to-face interactions, and dynamic versus static help resources. This categorisation reflects the complexity of modern learning environments where students might seek help through various channels, from traditional face-to-face consultations with instructors to online discussion boards and digital learning resources. Such conceptions are also particularly relevant now as digital technologies in and around HE, increasingly blur the boundaries between human and non-human sources of support.

Informed by these key AHS authors, this paper reports whether contemporary digital AHS studies within HE contexts are theoretically aligned to one of the following three AHS conceptions that we have devised. These are ‘social bounded’ considering and incorporating only direct human-to-human and/or digitally mediated human interactions, ‘informational bounded’ considering and incorporating only support via a digital system and ‘integrated’ considering and incorporating all types of support. While this categorisation necessarily simplifies complex realities, it provides a useful analytical framework for understanding how students AHS is currently being explored in HE. The specific research question on the help resource will seek to bring clarity to this area.

2.4. Students’ Requirements for Help with Digital Technologies

This review is motivated by a belief in the need to better understand the overlap between AHS and digital technologies in HE, particularly as evidence indicates students and wider society face challenges with digital technologies. Van Dijk’s (2020) digital divide framework helps explain the structural factors creating and maintaining digital inequalities in society, with students’ digital capabilities reflecting these broader patterns of disparity. While the experience with digital technology broadly encompasses an individual’s holistic engagement with digital technologies within the institutional context, it is mediated by a number of factors. Van Dijk conceptualises the digital divide within wider society through four key dimensions—motivational, physical access, skills, and usage. Van Dijk uses Tilly’s relational theory of inequality to explain how power dynamics and social relationships create and maintain digital disparities as much as individual behaviours, skills or motivations. This framework reveals how initial inequalities in digital access and skills create a cyclical pattern where those with advantages can maintain and amplify their position through control of digital knowledge and resources, while others may face more persistent barriers to developing digital competencies. The digital divide among students reflects these broader societal patterns of inequality.

There are a number of notable studies that focus on a digital gap in HE students. For instance, Barak (2018) reveals that only one-third of students possess deep knowledge of learning technologies and many lacking critical digital skills for academic work. In addition, students often lack sophisticated digital skills for academic and professional contexts, particularly in areas of critical analysis, evaluation, and ethical use of digital information (Morgan et al., 2022). Research shows many students continue to struggle to develop strong digital literacies on their own (Smith & Storrs, 2023), and while institutions offer various forms of support, students often do not take full advantage of these resources (Broadbent & Howe, 2023). The variation in students’ digital capabilities reflects systemic inequalities across motivation, access, skills, and usage patterns, which could indicate that digital competency challenges in HE are embedded in broader social structures rather than being issues that purely educational solutions can resolve. However, AHS could provide deeper insights and possible areas of development.

Previous research has demonstrated the importance of AHS behaviours, with evidence linking help-seeking to improved academic outcomes (Reeves & Sperling, 2015; Almaghaslah & Alsayari, 2022; Micari & Calkins, 2021). However, studies consistently show that learners who need the most help often do not seek it out (Karabenick & Knapp, 1988; Ryan et al., 2001), though when less confident students do successfully reach out for support, they show larger gains (Broadbent & Howe, 2023). Meta-analyses examining AHS are relatively rare, with the most recent focusing primarily on learning outcomes rather than exploring the complexities of how students engage with different help sources (Fong et al., 2023). This recent meta-analysis reveals that while instrumental AHS positively impacts learning, the effectiveness depends significantly on whether students seek help to master material versus simply obtaining quick solutions, highlighting the importance of understanding how students engage with help resources rather than just measuring frequency of AHS behaviours.

In summary, foundational AHS theories provide valuable research frameworks, however their application within studies remains under-examined. Students engage with multiple forms of assistance across human and digital domains, yet research appears to focus primarily on human-centred support. These factors highlight the need for a review to help develop the field and identify gaps for future research.

3. Materials and Methods

A scoping review was undertaken to provide an overview of the research literature using clear and replicable methods for selection and analysis (Cohen et al., 2018) and guided by the PRISMA framework (Page et al., 2021). Scoping reviews originated in the social sciences to help explore complex phenomena and can draw on heterogeneous data and varied methodological and epistemological traditions (Thomas, 2020). Because of this they are particularly useful when exploring under-examined areas of research as they can include a diverse range of methodologies, broadening the knowledge base that can be drawn from and analysed (Arksey & O’malley, 2005). Crucially scoping reviews are not intended to inform policy, protocols or robust guidelines but instead to provide an understanding of the “lay of the land” (Colquhoun et al., 2014). The methodology used in this review is an adaptation of the five-step framework described by Arksey and O’malley (2005).

3.1. Pilot and Search Terms

Arksey and O’malley (2005) caution that it is possible for searches to yield a large number of irrelevant studies. It is therefore essential to ensure that the evidence retrieved is specific and relevant to the inquiry at hand (Brettle & Grant, 2004). Following discussions with an academic librarian about the educational research base plus the multidisciplinary interest in AHS the following databases were selected for review: ERIC, SCOPUS and the British Education Index.

An initial pilot search was undertaken using the terms “academic help seeking” OR “academic HS”. Unfortunately, this captured a small number of results. Following further consultation with an academic librarian the search terms in

Table 1. Search terms.

Term and Variations	Boolean Operators
(“help seeking” OR “help-seeking”)	AND
(“digital” OR “technolog*”)

were selected and entered into the databases. The terms “help seeking” OR “help-seeking” and “digital OR technolog*” were anticipated to capture a wide range of AHS studies whether reporting on digital interventions, a digitally enabled or supported programme, digital skills development or digital help sources.

3.2. Study Screening and Selection

Once studies were identified a screening process was employed using the inclusion and exclusion criteria shown in

Table 2. Inclusion and exclusion criteria.

Inclusion Criteria	Exclusion Criteria
AHS as a focus	HS in a clinical context
Some exploration of the relationship between digital technology and AHS	No exploration of digital technology and AHS relationship
University undergraduate participants	School student participants
Articles published from 2019 (last 5 years)	Articles published before 2019
Written in English	Written in any other language
Peer reviewed journal articles	Grey literature, reviews, and thesis

.

Database searches conducted on the 23rd of February 2024 with limiters (2019–2024, peer-reviewed journals, full-text) returned 678 results published in the last 5 years. After removing duplicates (653 remaining), title/abstract screening reduced this to 53 studies. Full-text review yielded 25 studies meeting inclusion criteria. Figure 1 shows the complete PRISMA process.

3.3. Information Extraction

A list of the included studies after applying the inclusion and exclusion criteria are provided in

Table 3. Included studies.

References	Title	Objective	Participants	Study Design
(Acosta-Gonzaga & Ramirez-Arellano, 2021)	The influence of motivation, emotions, cognition, and metacognition on students’ learning performance: A comparative study in higher Education in Blended and Traditional Contexts	To compare the relationships between motivation, emotions, cognition, metacognition and students’ academic achievements across face-to-face and a blended learning context.	222 management and industrial engineering students and 116 biology students	Quantitative: quasi-experimental, survey, data traces, exam
(Adams et al., 2023)	From novice to navigator: Students’ academic help-seeking behaviour, readiness, and perceived usefulness of ChatGPT in learning	Investigate students’ readiness to use ChatGPT, and its perceived usefulness for academic purposes.	373 students from different fields	Mixed: survey
(An et al., 2022)	Self-regulated learning strategies for nursing students: A pilot randomised controlled trial	Compare the effects of the use of augmented reality as an innovative learning method and the use of a textbook as a conventional learning method.	62 nursing students from two universities	Quantitative: experimental, survey
(Barrot et al., 2021)	Students’ online learning challenges during the pandemic and how they cope with them: The case of the Philippines	Investigate the challenges and specific strategies that students employed to overcome them during COVID-19 enforced online learning.	200 students across psychology, physical education, and sports management	Mixed: survey, focus group
(Broadbent & Lodge, 2021)	Use of live chat in higher education to support self-regulated help seeking behaviours: a comparison of online and blended learner perspectives	Explore the use of live chat technology for online academic help-seeking within higher education focusing on any differences between online or blended learning student perspectives.	246 psychology students	Mixed: survey
(Bui et al., 2022)	Effectiveness of technology-integrated project-based approach for self-regulated learning of engineering students	Explore the impact of a technology-integrated project-based learning intervention (ReaLabs) on students’ learning attitudes, behaviours and self-regulated learning.	99 engineering students	Quantitative: experimental, survey
(Bull et al., 2020)	Using an innovative intervention to promote active learning in an introductory microbiology course	Measure the impact of a group PowerPoint poster creation activity on learning approaches, and content retention, as well as detect attitudes and actions consistent with increasing student engagement.	238 microbiology students	Mixed: quasi-experimental, focus group, survey
(Chen & Hwang, 2019)	An IRS-facilitated collective issue-quest approach to enhancing students’ learning achievement, self-regulation and collective efficacy in flipped classrooms	Investigate the impacts of an Instant Response System (Kahoot!) facilitated collective issue-quest strategy on students’ learning achievement, self-regulation, learning satisfaction and collective efficacy.	85 internet marketing students	Quantitative: quasi-experimental, survey
(Chou & Chang, 2021)	Developing adaptive help-seeking regulation mechanisms for different help-seeking tendencies	Propose an approach for identifying students’ different help-seeking tendencies and evaluate the effect of adaptive AHS regulation on students’ AHS and learning performance.	52 computer science students	Quantitative: experimental, test, survey
(Esparza Puga & Aguilar, 2023)	Students’ perspectives on using YouTube as a source of mathematical help: the case of ‘julioprofe’	Explore students’ perspectives on the use of videos from the YouTube channel julioprofe as a source of mathematical help.	22 engineering students	Qualitative: focus group
(Evenhouse et al., 2020)	Motivators and barriers in undergraduate mechanical engineering students’ use of learning resources	Examine the motivators and barriers impacting students’ choices to engage with class-provided learning resources.	26 engineering dynamics students	Qualitative: interview
(Kahu et al., 2022)	‘A sense of community and camaraderie’: Increasing student engagement by supplementing an LMS with a Learning Commons Communication Tool	Gain an in-depth understanding of how Discord and Microsoft Teams work in conjunction with an LMS to benefit student engagement and learning.	19 students within a computer sciences and information technology department	Qualitative: interview
(Meşe & Mede, 2023)	Using digital differentiation to improve EFL achievement and self-regulation of tertiary learners: the Turkish context	Explore the effect of Differentiated Instruction on students’ English Foreign Language achievement in terms of speaking proficiency and SRL in an intact classroom.	31 English as foreign language students	Mixed: quasi-experimental, test, survey, focus group
(Onah et al., 2022)	Investigating self-regulation in the context of a blended learning computing course	Investigate aspects of blended MOOC usage in the context of a computing course.	27 then 17 students in computer security	Quantitative: survey
(Önder & Akçapınar, 2023)	Investigating the effect of prompts on learners’ academic help-seeking behaviours on the basis of learning analytics	Examine the effect of prompts (meaning questions or elicitations that aim to encourage learning strategies that students are capable of but do not show spontaneously) on fostering learners’ online AHS behaviour using learning analytics approaches.	39 online robotic programming students	Quantitative: experimental, data logs
(Papamitsiou & Economides, 2021)	The impact of on-demand metacognitive help on effortful behaviour: A longitudinal study using task-related visual analytics	Investigate changes in learners’ effortful behaviour over time, due to receiving metacognitive help in the form of task-related visual analytics.	67 students on a management information systems course, took part in all 4 phases (more overall)	Quantitative: quasi-experimental, test
(Radzitskaya & Islamov, 2024)	Nanolearning approach in developing professional competencies of modern students: Impact on self-regulation development	Determine nanolearning effectiveness in the context of students’ self-regulation development.	120 sociology students	Quantitative: quasi-experimental, test, survey
(Rothstein et al., 2023)	Collaborative engagement and help-seeking behaviours in engineering asynchronous online discussions	Investigate the ways students use discussion forums to engage with their peers and course material, how this contributes to a sense of community and whether this is sufficient to facilitate group and individual knowledge acquisition.	796 engineering students	Qualitative: discussion forum posts
(Schlusche et al., 2023)	Understanding students’ academic help-seeking on digital devices—a qualitative analysis	Determine requirements for the design of digital services that support asking for and receiving help.	59 students mostly from media informatics and psychology	Qualitative: interview
(Viriya, 2022)	Exploring the impact of synchronous, asynchronous, and bichronous online learning modes on EFL students’ self-regulated and perceived English language learning	Investigate the influences of synchronous, asynchronous, and bichronous learning modes on students’ self-regulated and perceived learning when learning English language online.	142 foundation English students	Mixed: survey, learning diary
(Wang et al., 2023)	Cognitive load patterns affect temporal dynamics of self-regulated learning behaviours, metacogntive judgments, and learning achievements	Investigate how students’ cognitive load patterns relate to their self-regulated learning and performance.	111 medical students	Quantitative: quasi-experimental, data logs, test
(Wu, 2021)	Learning analytics on structured and unstructured heterogeneous data sources: Perspectives from procrastination, help-seeking, and machine-learning defined cognitive engagement	Investigate how students’ demographic characteristics, motivational tendencies, and their cognitive engagement in meaningful statistics learning on Facebook are associated with their academic achievement.	78 advanced statistics students	Quantitative: survey, machine learning categorised and counted posts, test
(Yau & Chan, 2023)	The investigation of Hong Kong university engineering students’ perception of help-seeking with attitudes towards learning simulation software	Investigate students’ help-seeking perceptions and attitudes towards learning simulation software.	127 engineering students	Quantitative: survey
(L. Zhang et al., 2023)	Facilitating student engagement in large lecture classes through a digital question board	Explore how the presence of a digital question board influences students’ cognitive engagement and emotional engagement.	253 introductory research methodology students	Mixed: quasi-experimental, survey, interview, observation, online posts
(Z. Zhang et al., 2023)	Comparing blended and online learners’ self-efficacy, self-regulation, and actual learning in the context of educational technology	Investigate disparities in learning among preservice teachers in the realm of educational technology, specifically in relation to different learning modes (blended vs. fully online).	325 pre-service teachers	Quantitative: quasi-experimental, survey

.

A deductive approach was taken for the initial analysis. The 25 studies were compiled in Zotero. Then the information was systematically extracted by both authors in two comprehensive Excel spreadsheets under the following categories: Author(s), year, title, objective, study design, participants, country, digital focus, conceptual frameworks, AHS definition, social bounded, informational bounded or integrated conception of AHS, sources of help identified, factors identified as influencing AHS, main findings and journal name. Where there was disagreement between the authors, this was discussed, if no agreement was found the study was re-read and following further discussions a consensus was reached. Following are the results of the analysis.

4. Results

The following results address the three research questions: contemporary literature characteristics (popularity, methods, contexts), theoretical grounding and frameworks, and help sources across social bounded, informational bounded, and integrated approaches.

4.1. Topic Popularity

The interest in AHS in HE, specifically including a digital aspect, seems to be growing, showing an upward trend (Figure 2).

4.2. Research Methods

Overall, thirteen studies employed quantitative methods, as can be seen in Table 1, making it the most common, seven employed mixed methods and five qualitative methods. Participant numbers ranged from 19 undergraduates involved in qualitative interviews (Kahu et al., 2022) to 796 undergraduates whose discussion board posts were reviewed via qualitative content analysis (Rothstein et al., 2023). The most popular method for gathering data was the survey, with Pintrich and De Groot’s Motivated Strategies for Learning Questionnaire (MSLQ) (Pintrich & De Groot, 1990) and Barnard et al.’s Online Self-regulated Learning Questionnaire (OSLQ) (Barnard et al., 2009) regularly employed in whole or adapted form.

4.3. Geographic and Discipline Contexts

Given the English language limitations of the review some pertinent studies could have been missed, however Figure 3 shows wide geographical distribution.

Table 4. Digital aspects and theoretical alignments.

Digital Focus of the Study’s Design	Conception of AHS	Sources of Help Reported	Country	References
DC—PowerPoint poster creation	Social	Peers, instructors and teaching assistants	Canada	(Bull et al., 2020)
DE—online differentiated instruction	Social	Peers and tutors via Zoom	Turkey	(Meşe & Mede, 2023)
DE—blended compared to traditional	Social	None explicitly given (assumption that AHS is provided through the Moodle virtual learning environment tools)	Mexico	(Acosta-Gonzaga & Ramirez-Arellano, 2021)
DE—compared three modes of online learning	Social	Face-to-face friends, tutors, peers or family members, online call or live chat, instructors through email and messages	Thailand	(Viriya, 2022)
DE—digitally rich environment	Integrated	Lecturers, teaching assistant help room, peers, digital messaging, social media blog, discussion forum, course solution videos and digital lecture book	Turkey	(Evenhouse et al., 2020)
DE—emergency distance learning	Integrated	Internet, Facebook groups, family members, home resources and teachers	Philippines	(Barrot et al., 2021)
DE—online and blended mode comparison on a digital skills course	Social	Peers, instructors, and teaching assistants in the course	USA	(Z. Zhang et al., 2023)
DE—tracking of interactions in Bioworld intelligent tutoring system	Information	Course documents and programme library	China	(Wang et al., 2023)
DR—LiveChat system in two contexts, fully online and blended	Social	LiveChat system, email and discussion board	Australia	(Broadbent & Lodge, 2021)
DR—simulation software (FlexSim and Arena)	Integrated	Calling, emailing, asking face-to-face or on live chat or posting question on discussion board to instructor or peers and ‘proper’ website support	Hong Kong	(Yau & Chan, 2023)
DR—adaptive computer assisted learning system	Integrated	Computer assisted learning system	Taiwan	(Chou & Chang, 2021)
DR—asynchronous online discussions	Social	Discussion board	USA	(Rothstein et al., 2023)
DR—augmented reality compared to a textbook as a method to support SRL	Integrated	Augmented reality app and textbook	Korea	(An et al., 2022)
DR—computer mediated communication tools	Integrated	Instant messenger, WhatsApp, bulletin board, Moodle, document exchange service, Facebook	Germany	(Schlusche et al., 2023)
DR—digital AHS prompts	Integrated	Digital AHS prompts, lecture recordings, recommended resources, discussion forums, and synchronous sessions	Turkey	(Önder & Akçapınar, 2023)
DR—Discord, Microsoft Teams and a learning management system	Social	Peers and instructors through the Discord and Teams communication tools	New Zealand	(Kahu et al., 2022)
DR—Facebook community	Social	Facebook mediated peer and instructor help	Taiwan	(Wu, 2021)
DR—Kahoot integration in a flipped classroom	Integrated	Kahoot, peers and internet	Taiwan	(Chen & Hwang, 2019)
DR—massively open online course as online aspect of blended learning	Social	Massively Open Online Course (MOOC)	UK	(Onah et al., 2022)
DR—Padlet backchannel	Social	Peers and lecturers in class and on a Padlet	China	(L. Zhang et al., 2023)
DR—ReaLabs software	Social	ReaLabs software and Google Docs	Vietnam	(Bui et al., 2022)
DR—students’ readiness and perceived usefulness of ChatGPT for learning	Integrated	ChatGPT	Malaysia	(Adams et al., 2023)
DR—task related visual analytics to aid AHS	Integrated	Task visualisations	European university	(Papamitsiou & Economides, 2021)
DR—TikTok video support as part of microlearning approach	Social	Video, social media and TikTok	Republic of Kazakhstan	(Radzitskaya & Islamov, 2024)
DR—YouTube channel (Julioprofe)	Integrated	YouTube (Julieprofe channel)	Mexico	(Esparza Puga & Aguilar, 2023)

lists each studies country of origin, highest were Turkey and Taiwan with three in each, this was followed by the USA, China and Mexico with two each. With regards to disciplines, a broad range of undergraduate educational contexts were represented including microbiology, engineering, computer science, computer security, robotics programming, advanced statistics, management information, English as a foreign language, media informatics, psychology, nursing, teacher education, physical education, sports management and medicine. Engineering was the most represented of the disciplines within the captured literature with five studies situated within that context.

4.4. Theoretical Frameworks

In order to understand the different conceptualisations of AHS within the literature we looked for explicit mentions of theories or theorists. The most prevalent theory referenced throughout the literature was self-regulated learning (SRL) with 18 studies (Acosta-Gonzaga & Ramirez-Arellano, 2021; Adams et al., 2023; An et al., 2022; Broadbent, 2017; Bui et al., 2022; Chen & Hwang, 2019; Chou & Chang, 2021; Esparza Puga & Aguilar, 2023; Kahu et al., 2022; Meşe & Mede, 2023; Onah et al., 2022; Önder & Akçapınar, 2023; Radzitskaya & Islamov, 2024; Viriya, 2022; Wang et al., 2023; Wu, 2021; Yau & Chan, 2023; Z. Zhang et al., 2023) describing or defining it. Where SRL was detailed in a study AHS would usually be referred to as a learning strategy or dimension within SRL e.g., (Broadbent & Lodge, 2021). While some papers reference SRL definitions from sources like Zimmerman (1989) without further elaboration, others expand the concept to include digital resources (Chou & Chang, 2021) or provided more detailed definitions encompassing specific actions (Wang et al., 2023), cognitive and emotional processes (Wu, 2021), or social interactions (Meşe & Mede, 2023).

Direct references to theories of AHS were less prevalent and harder to identify. Of the 25 studies reviewed, only eight explicitly referenced established AHS theories or theorists: two cited Karabenick (Chou & Chang, 2021; Önder & Akçapınar, 2023; Schlusche et al., 2023; Wu, 2021), two referenced Nelson-Le Gall (Schlusche et al., 2023; Yau & Chan, 2023), and one drew upon Puustinen’s work (L. Zhang et al., 2023). This relatively low number of clear theoretical references to foundational AHS literature suggests that many studies approached help-seeking without directly engaging with its established theoretical foundations.

In addition to AHS and SRL, a wide range of other models and theories were drawn upon to form the conceptual frameworks employed across the studies. This included control-value theory of achievement emotions (Acosta-Gonzaga & Ramirez-Arellano, 2021), problem based learning (Bui et al., 2022), deep and surface learning and active learning (Bull et al., 2020), social constructivism (Evenhouse et al., 2020), framework of student engagement (Kahu et al., 2022), differentiated instruction (Meşe & Mede, 2023), metacognition (Papamitsiou & Economides, 2021), nanolearning (Radzitskaya & Islamov, 2024), computer supported collaborative learning (Rothstein et al., 2023), perceived learning (Viriya, 2022), cognitive load theory (Wang et al., 2023), personal learning environments (Wu, 2021), the technology acceptance model (Yau & Chan, 2023) and social constructivist learning (L. Zhang et al., 2023).

4.5. Digital Technology Focus of the Study’s Design

To further identify potential gaps, we analysed how digital technology was framed as the focus of each study’s design. We inductively identified three categories of digital focus encompassing each study, which are presented in the first column of Table 4. Of these, digital technology was found to be incorporated as either the catalyst for help-seeking ‘digital creation’ (DC) where students needed assistance with new digital tools or systems, or as the medium for obtaining help ‘digital resources’ (DR) where digital resources served as sources of support, or finally as a digital teaching environment or modality ‘digital environment’ (DE) where a digital environment or digitally enabled teaching approach was explored and sometimes compared to a non-digital equivalent. For the former, Bull et al. (2020) presented students with an open-ended digital creation task and then explored the influence this had on AHS, deep learning and motivation. This was the only study to take such an approach.

Seventeen studies aligned to the second category of digital resources focusing upon one or more digital systems such as live chat (Broadbent & Lodge, 2021) or Kahoot (Chen & Hwang, 2019) and specifically, reporting the influence they had on AHS or an encompassing framework such as SRL (Adams et al., 2023; An et al., 2022; Broadbent & Lodge, 2021; Bui et al., 2022; Chen & Hwang, 2019; Chou & Chang, 2021; Esparza Puga & Aguilar, 2023; Kahu et al., 2022; Onah et al., 2022; Önder & Akçapınar, 2023; Papamitsiou & Economides, 2021; Radzitskaya & Islamov, 2024; Rothstein et al., 2023; Schlusche et al., 2023; Wu, 2021; Yau & Chan, 2023; L. Zhang et al., 2023). Studies aligned to the second category demonstrated a digital intervention orientation usually focused on exploring or explaining the impact a digital resource had on AHS and other frameworks. Finally, seven studies attempted to explore or explain the influence of different digital environments or teaching modalities on AHS one of which would be digitally mediated (Acosta-Gonzaga & Ramirez-Arellano, 2021; Barrot et al., 2021; Evenhouse et al., 2020; Meşe & Mede, 2023; Viriya, 2022; Wang et al., 2023; Z. Zhang et al., 2023). These were aligned to the third category. There were no studies that looked at how students naturally seek help for digital challenges in their everyday academic experiences, revealing a gap in our understanding of spontaneous help-seeking behaviours in digital learning environments.

4.6. Social Bounded, Informational Bounded or Integrated Conceptions

We categorised each study’s conceptualisation of help (as shown in column 2 of Table 4) according to whether it was primarily social bounded, informational bounded, or integrated in its approach. However, there was complexity in categorising some studies due to limited descriptions alongside a lack of direct references to AHS models or definitions. This was tackled through iterations of reviewing and discussing by the authors. Categorization used the following criteria:

• Social bounded—study describes AHS as human-to-human or digitally mediated human-to-human and incorporates related AHS frameworks, methods and help sources.
• Informational bounded—study describes AHS as seeking information from digital sources only.
• Integrated—study describes AHS as inclusive of human-to-human and human-to-non-human and incorporates an integrated framework of AHS or a mix of frameworks, methods and help sources that suggest an integrated interpretation.

Where the overall study was not internally consistent or clear, displaying a mix of social and information AHS definitions, methods or help sources, it was categorised as an integrated conception of AHS. Across the body of literature, we agreed that thirteen studies indicated a social bounded conception of AHS making it, just, the most popular underlying conception. Eleven studies embraced an integrated conception of AHS incorporating social and informational sources of help. One study demonstrated an informational bounded conception of AHS providing this limited description of AHS “Students seek for the online library (embedded in BioWorld) to clarify what they do not know or confirm whether their thoughts are correct” (Wang et al., 2023, p. 6).

To illustrate some of the difficulties categorising these studies, twelve studies drew on wholly or in adapted form, two popular surveys. The MSLQ (Acosta-Gonzaga & Ramirez-Arellano, 2021; Bull et al., 2020; Wu, 2021) and the more contemporary OSLQ (An et al., 2022; Bui et al., 2022; Chen & Hwang, 2019; Meşe & Mede, 2023; Onah et al., 2022; Papamitsiou & Economides, 2021; Radzitskaya & Islamov, 2024; Viriya, 2022). These surveys have a social bounded aspect to their four AHS related questions. Six studies (Acosta-Gonzaga & Ramirez-Arellano, 2021; Barrot et al., 2021; Bui et al., 2022; Chen & Hwang, 2019; Meşe & Mede, 2023; Onah et al., 2022) provided no clear definition of AHS, but of those six, five employed these instruments. Four of these were internally consistent with the instruments aligning to more traditional social bounded conceptualisations of AHS. The other two were more difficult to categorise. Chen and Hwang (2019) for example, despite using the OSLQ, included ‘internet searching’ as one of the student sources of help, this was part of the intervention design and suggested a more integrated conception of AHS. Whilst Barrot et al. (2021), provided no clear definition and used neither the MSLQ or OSLQ, but from the focus group reported family members, the internet and home resources as sources of help. Therefore, this study was categorised as employing an integrated conception of AHS as well.

Studies that were more explicit in detailing their understanding of AHS also sometimes required discussion between the authors. For example, An et al. (2022) defined AHS as a component of SRL specifically involving seeking assistance from others yet proceeded to explore the influence of an augmented reality application on SRL and AHS. We therefore categorised this as employing an integrated conception of AHS, due to the mix of the social AHS definition given alongside the studies focus upon two relatively static information sources of help, an augmented reality application and a textbook. Many of the study descriptions of AHS were not detailed, sometimes containing only one sentence. The remaining studies provided definitions of AHS that, we agreed, were internally consistent with other aspects of those studies.

4.7. Sources of Help

To provide an overview of available help sources, we collected each form of support mentioned across the studies, ranging from human interactions (peers, teachers) to digital resources (discussion boards, live chat, static web pages) and hybrid solutions (moderated forums, interactive videos). This allowed us to identify which help sources are being studied and where potential gaps might exist (see Table 4 column 3). A rich range of help sources were identified across the literature. Studies employing a social bounded conception of help such as Viriya (2022) only presented sources that offered a relatively immediate social help dimension with lecturers, peers, friends, family or anonymous responders providing help face-to-face or mediated by technology. The mediating technologies referenced in these studies included: discussion boards, live chat, email, phone calls, video conferencing/virtual classrooms (MS Teams, RealLabs) and various social media (WhatsApp, Facebook, Discord, TikTok). The single study (Wang et al., 2023) describing only an informational bounded aligned conception of AHS presented two sources of help: course documents and a programme library. Studies demonstrating a more integrated conception of AHS presented a more diverse selection of help sources including face-to-face, digitally mediated social bounded sources and informational sources such as: on screen hints and automated prompts, audience response systems (Kahoot), discussion/bulletin boards, task visualisations, instant messenger, various social media (WhatsApp, Facebook), document exchange service, ChatGPT, email, phone calls, website support, help rooms, blogs, course solution videos and digital ‘lecturebooks’. Surprisingly only one study (Adams et al., 2023) was identified that focused on a GenAI tool (ChatGPT).

5. Discussion

5.1. Q1: What Is the Nature of Contemporary HE AHS Literature Containing a Digital Aspect?

The number of studies published in the field is growing, covering a wide range of subject areas and the dominant methodological approach is quantitative. A broader spectrum of approaches may help develop the conceptualisation of AHS within digitally rich HE contexts. Future research employing longitudinal studies and qualitative methods could offer richer insights into the AHS behaviours of academically and digitally challenged students. The geographical location of the studies also points towards a dominance of high economic status and/or westernised countries. AHS as an idea appears to be widely accepted across many cultures; however, increased representation of students in different locations may help to extend existing conceptual frameworks and understanding of the utility of AHS.

5.2. Q2: To What Extent Are These Studies Grounded in Theory and How Are These Theoretical and Conceptual Frameworks Described?

The theory of SRL dominates this part of the AHS research landscape. This means that a range of SRL dimensions such as goal setting and time management, not just AHS tend to be investigated. Though surveys have contributed to our understanding of help-seeking, Fong et al.’s (2023) meta-review highlights their limitations in capturing AHS complexity. Future studies could focus their resources by examining AHS in greater detail, giving it more space, rather than including it alongside the other SRL dimensions. More critically, the theoretical and conceptual underpinnings of these studies were frequently found to be vaguely described or completely missing, with many of the studies only lightly referencing established AHS definitions and frameworks. Therefore, AHS and digital technologies in HE appears under theorised.

To identity further theoretical gaps in the AHS literature, we mapped the different conceptualisations of AHS on to the AHS stages discussed by Karabenick and Dembo (2011) (see

Table 5. Mapping studies to Karabenick and Dembo’s 8 stages.

Karabenick and Dembo Dimension 8 Steps	Studies Aligned with These Processes (* Indicates Studies Associated with MSLQ (** Indicates Studies Associated with OSLQ)	MSLQ Survey Questions	OSLQ Survey Questions
Decide whether a problem exists	(Adams et al., 2023) (Chou & Chang, 2021) (Schlusche et al., 2023)
Decide whether assistance is required or desired	(Adams et al., 2023) (Barrot et al., 2021) (Schlusche et al., 2023) (Z. Zhang et al., 2023)
Determine whether to request assistance	(Adams et al., 2023) (Schlusche et al., 2023) (Acosta-Gonzaga & Ramirez-Arellano, 2021) * (Bull et al., 2020) * (Wu, 2021) *	Even if I have trouble learning the material in this class, I try to do the work on my own, without help from anyone.
Choose the type of help you want (executive or instrumental)	(Chou & Chang, 2021) (Schlusche et al., 2023)
Choose the person [resource] you want to seek for help	(Broadbent & Lodge, 2021) (Chou & Chang, 2021) (Esparza Puga & Aguilar, 2023; Evenhouse et al., 2020) (Önder & Akçapınar, 2023) (Kahu et al., 2022) (L. Zhang et al., 2023) (Acosta-Gonzaga & Ramirez-Arellano, 2021) * (Bull et al., 2020) * (Wu, 2021) * (An et al., 2022) ** (Bui et al., 2022) ** (Chen & Hwang, 2019) ** (Meşe & Mede, 2023) ** (Onah et al., 2022 **; Radzitskaya & Islamov, 2024) ** (Viriya, 2022) **	I try to identify students in this class whom I can ask for help if necessary.	I find someone who is knowledgeable in course content so that I can consult with him or her when I need help.
Ask for help	(Adams et al., 2023) (Broadbent & Lodge, 2021) (Esparza Puga & Aguilar, 2023) (Evenhouse et al., 2020) (Kahu et al., 2022) (Önder & Akçapınar, 2023) (Papamitsiou & Economides, 2021) (Rothstein et al., 2023) (Schlusche et al., 2023) (Wang et al., 2023) (Yau & Chan, 2023) (L. Zhang et al., 2023) (Z. Zhang et al., 2023) (Acosta-Gonzaga & Ramirez-Arellano, 2021) * (Bull et al., 2020) * (Wu, 2021) * (An et al., 2022) ** (Bui et al., 2022) ** (Chen & Hwang, 2019) ** (Meşe & Mede, 2023) ** (Onah et al., 2022 **; Radzitskaya & Islamov, 2024) ** (Viriya, 2022) **	I ask the instructor to clarify concepts I don’t understand well. When I can’t understand the material in this course, I ask another student in this class for help.	I share my problems with my classmates online so we know what we are struggling with and how to solve our problems. If needed, I try to meet my classmates face-to-face. I am persistent in getting help from the instructor through e-mail.
Get help	(Adams et al., 2023) (Broadbent & Lodge, 2021) (Esparza Puga & Aguilar, 2023) (Evenhouse et al., 2020) (Kahu et al., 2022) (Önder & Akçapınar, 2023) (Papamitsiou & Economides, 2021) (Rothstein et al., 2023) (Schlusche et al., 2023) (Yau & Chan, 2023) (Wang et al., 2023) (L. Zhang et al., 2023) (Z. Zhang et al., 2023)
Process the help you got	(Adams et al., 2023) (Esparza Puga & Aguilar, 2023) (Evenhouse et al., 2020) (Schlusche et al., 2023) (Z. Zhang et al., 2023)

). We adapted item 5 to include ‘resources’ as well as ‘person’ in order to encompass digital systems. Using this newer conceptualisation to analyse the studies, it is possible to place the studies across this dimension and indicate areas potentially requiring further exploration. As many of the studies used MSLQ and OSLQ instruments, we also included the specific AHS related questions from these surveys in the table. This again was made difficult due to the previously described limitations in the theoretical detail the corpus provided. Studies that presented limited methodological information, such as no interview or survey protocols, were more difficult to categorise.

Placing these studies on the dimension exposed some gaps. For instance, overall, we found studies tended to focus on the later stages of the AHS process rather than the antecedents. Though, fewer studies considered the final step and even fewer reference the idea of types of help. Considering this, without an understanding of the early stages, studies will fail to capture how many students need help but then decide not to go on and seek it, nor go on to explore what might be the factors influencing this. Additionally, only Chou and Chang (2021) and Schlusche et al. (2023) explored the idea of help-avoidance (Karabenick, 2003). Furthermore, we can see that receiving help is not the end of the process, the final stages of gauging the utility and reliability of that help and doing something with it needs further exploration. Only the five studies included reference the concepts of executive and instrumental; therefore, across the literature, the types of help transactions and the importance of these with regards to the long- and short-term gains for the help seeker are areas that could benefit from further investigation.

5.3. Q3: Which Sources (Or Resources) of Help Are Identified Across This Literature?

The findings section presents the full range of help sources identified in the literature. These also indicate possible gaps in the ways that help is conceptualised, particularly in how the literature heavily focuses on predetermined digital interventions and resources. Only one study (Bull et al., 2020) examined AHS in response to an open-ended digital creation task set by a teacher, and notably, no studies investigated how students seek help for digital challenges in their everyday academic experiences. These gaps suggest that our understanding of help sources is largely confined to controlled, intervention-based scenarios rather than the full spectrum of AHS behaviours that occur in authentic HE environments. As previously identified, there is a tendency of the literature to create a support resource and point students to it in order to measure its effectiveness in supporting students (Maclaren, 2017).

Our review also revealed a split in the literature, with a slight majority of studies demonstrating a social bounded interpretation of AHS compared to an integrated one. This is somewhat surprising given that the scoping review purposefully captured studies with a digital dimension in the title or abstract. This supports similar limitations reported by Giblin et al. (2021) and raises questions about whether such AHS literature adequately captures modern learning behaviours and opportunities for digital help in HE. Furthermore, researchers could be clearer about what their theoretical framing includes and excludes. Practitioners reading the extant literature might not conceive that participants in such studies were not given the scope to talk about help sought and provided by digital sources, from institutional webpages and YouTube to even more broadly the internet. Interestingly only one study captured in this scoping review focused on GenAI as a potential source for help, this may relate to the time required to undertake and publish empirical research and when this scoping review was undertaken (early 2024). However, this may also be an outcome of the popularity of socially framed concepts of AHS. More explicit use of integrated AHS frameworks, as proposed in work by Makara and Karabenick (2013) and Puustinen and Rouet (2009), could offer better contextual alignment within the increasingly digital HE environment.

5.4. Critical Analysis of Research Trends

The patterns revealed in this review extend beyond methodological observations to questions about how academic help-seeking research serves the concerns of supporting students in increasingly complex digital learning environments.

When theoretical engagement is limited and research focuses primarily on tool effectiveness rather than behavioural understanding, we risk diminishing the practical value of AHS frameworks. This concern becomes especially relevant given that leading AHS theorists identified the need for integrated digital frameworks over a decade ago (Puustinen & Rouet, 2009; Makara & Karabenick, 2013), yet our findings suggest the field has not meaningfully engaged with these calls for theoretical development.

This review identifies systematic patterns that collectively constrain the field’s ability to develop insights that could improve student support: intervention-driven research focus (17 of 25 studies), SRL theoretical dominance (18 of 25 studies), and limited engagement with AHS-specific theory. When 17 studies focus on evaluating predetermined digital interventions rather than understanding authentic help-seeking behaviours, we measure resource or tool effectiveness while missing the behavioural insights needed to design support systems. When help-seeking becomes subordinated to broader SRL frameworks without deeper engagement with AHS-specific theories, we lose the nuanced understanding of why students avoid seeking available help. Studies frequently provide vague or missing descriptions of their conceptual underpinnings, with many only lightly referencing established AHS definitions and frameworks. This limitation aligns with broader calls for more naturalistic research in this area (Evenhouse et al., 2020; Maclaren, 2017).

Perhaps most critically, our mapping of studies onto Karabenick and Dembo’s (2011) AHS process reveals concentration in later stages while early-stage behaviours—including problem recognition and help-avoidance—remain largely unexplored. This gap means we cannot understand why students avoid or fail to seek help in the first place, limiting our ability to design interventions that address these barriers. To support students in navigating their evolving help landscapes, understanding these early-stage decisions becomes essential as artificial intelligence becomes increasingly integrated into educational contexts. The executive versus instrumental help-seeking framework offers a ready-to-use conceptual tool for exploring these dynamics, as these distinct motivations produce different learning outcomes—yet our analysis reveals minimal engagement with this framework in digital contexts where the implications for student learning may be particularly significant.

These patterns suggest the field may benefit from strengthening theoretical engagement to advance understanding of how digital contexts impact help-seeking behaviours. The result is research that serves immediate intervention evaluation needs while failing to build the theoretical foundations required for long-term improvements in student support systems.

6. Conclusions

This scoping review explored the intersection of AHS and digital technology in HE. Our findings indicate a growing interest in this area. With regards to gaps, methodologically, there is a predominance of quantitative approaches. Theoretically, SRL is employed as the main theoretical framework. AHS is often positioned as a component within SRL rather than a standalone concept. This latter trend, while valuable, may limit the understanding of AHS as a distinct phenomenon in contemporary HE contexts. Furthermore, we suggest alignment to social bounded conceptions of AH, may be increasingly incongruent with contexts containing ever more accessible and adaptive digital help sources. We suggest that when applied to contemporary HE contexts, where this conceptual bounding is not made explicit, there is a risk that practitioners may not appreciate what is being occluded. Equally, for clarity and to increase awareness, AHS studies employing a human and non-human integrated lens should make this clear alongside the reasons why their approach branches from earlier frameworks. Our own mapping of studies onto a modified and integrated version of Karabenick and Dembo’s (2011) AHS process model exposed gaps in current research within this intersection, particularly in the early stages of AHS and in understanding help-avoidance behaviours. With regards to the sources of help identified, we found they tended to span a wide spectrum, from highly adaptive human and digitally mediated interactions to fixed-content non-human digital and physical resources. Those studies employing an integrated conception of AHS captured a wider range of help sources.

Future studies should address the identified gaps and, more generally, look to expand and specifically detail AHS conceptions engaged with. This review contributes to this goal by mapping the current intersection of AHS research and digital technology in HE contexts and by identifying potentially fruitful areas for future investigation and improvement, and the continuation of looking for answers.